Computer-aided segmentation of cardiac images obtained by various modalities plays an important role and is a prerequisite for a wide range of cardiac applications by facilitating the delineation of anatomical regions of interest. Numerous computerized methods have been developed to tackle this problem. Recent studies employ sophisticated techniques using available cues from cardiac anatomy such as geometry, visual appearance, and prior knowledge. In addition, new minimization and computational methods have been adopted with improved computational speed and robustness. We provide an overview of cardiac segmentation techniques, with a goal of providing useful advice and references. In addition, we describe important clinical applications, imaging modalities, and validation methods used for cardiac segmentation.

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Conventional stereoscopic displays present images on a single focal plane. The resulting mismatch between the stimuli to the eyes' focusing response (accommodation) and to convergence causes fatigue and poor stereo performance. One solution is to distribute image intensity across a number of widely spaced image planes-a technique referred to as depth filtering. Previously, we found this elicits accurate, continuous monocular accommodation responses with image-plane separations as large as 1.1 Diopters (D, the reciprocal of distance in meters), suggesting that a small number of image planes could eliminate vergence-accommodation conflicts over a large range of simulated distances. Evidence exists, however, of systematic differences between accommodation responses to binocular and monocular stimuli when the stimulus to accommodation is degraded, or at an incorrect distance. We examined the minimum image-plane spacing required for accurate accommodation to binocular depth-filtered images. We compared accommodation and vergence responses to changes in depth specified by depth filtering, using image-plane separations of 0.6 to 1.2 D, and equivalent real stimuli. Accommodation responses to real and depth-filtered stimuli were equivalent for image-plane separations of ∼ 0.6 to 0.9 D, but differed thereafter. We conclude that depth filtering can be used to precisely match accommodation and vergence demand in a practical stereoscopic display.

Stereoscopic displays must present separate images to the viewer's left and right eyes. Crosstalk is the unwanted contamination of one eye's image from the image of the other eye. It has been shown to cause distortions, reduce visual comfort, and increase perceived workload during the performance of visual tasks. Crosstalk also affects one's ability to perceive stereoscopic depth although little consideration has been given to the perception of depth magnitude in the presence of crosstalk. We extend a previous study (Tsirlin, Allison, and Wilcox, 2011) on the perception of depth magnitude in stereoscopic occluding and non-occluding surfaces to the special case of crosstalk in thin structures. We use a paradigm in which observers estimated the perceived depth difference between two thin vertical bars using a measurement scale. Our data show that as crosstalk levels increase, the magnitude of perceived depth decreases, especially for stimuli with larger relative disparities. In contrast to the effect of crosstalk on depth magnitude in larger objects, in thin structures a significant detrimental effect has been found at all disparities. Our findings, when considered with the other perceptual consequences of crosstalk, suggest that its presence in S3D media, even in modest amounts, will reduce observers' satisfaction.

Coarse integral volumetric imaging (CIVI) combines multiview and volumetric display solutions and presents undistorted floating 3D images by correcting distortion of volumetric image for each view. In the conventional CIVI with limited viewing angle, distortions of image planes can be approximated to be parabolic in the direction of depth, while those in horizontal and vertical directions can be ignored. When the viewing angle becomes wider, however, this approximation cannot realize presentation of the undistorted image. To cope with the strong distortions, the method that the authors propose calculates the z-coordinate of the generated real image in detail and depicts each pixel on the display panel of the corresponding depth. Also distortions in horizontal and vertical directions are corrected by using texture mapping. To attain precise correction in vertical, horizontal and depth directions, optical paths of light rays between the display panel and each viewpoint are calculated with an optical simulator. Color aberration can also be corrected by mapping red, green and blue textures separately based on the result of the optical simulation.

As the production of stereoscopic content increases, so does the need for post-production tools for that content. Video inpainting has become an important tool for rig removal, but there has been little consideration of the problem in stereo. We present an algorithm for stereo-video inpainting that builds on existing exemplar-based video completion and also considers the issues of view consistency. Given user-selected regions in the sequence, which may be in the same location in several frames and in both views, the objective is to fill in this area using all the available picture information. Existing algorithms lack temporal consistency, causing flickering and other artifacts. We explore the use of long-term picture information across many frames in order to achieve temporal consistency at the same time as exploiting inter-view dependencies within the same framework.

A stereoscopic 3-D version of the film Avatar was shown to 85 people who subsequently answered questions related to sickness, visual strain, stereoscopic image quality, and sense of presence. Viewing Avatar for 165 min induced some symptoms of visual strain and sickness, but the symptom levels remained low. A comparison between Avatar and previously published results for the film U2 3D showed that sickness and visual strain levels were similar despite the films' runtimes. The genre of the film had a significant effect on the viewers' opinions and sense of presence. Avatar, which has been described as a combination of action, adventure, and sci-fi genres, was experienced as more immersive and engaging than the music documentary U2 3D. However, participants in both studies were immersed, focused, and absorbed in watching the stereoscopic 3-D (S3-D) film and were pleased with the film environments. The results also showed that previous stereoscopic 3-D experience significantly reduced the amount of reported eye strain and complaints about the weight of the viewing glasses.

A plenoptic camera is a natural multiview acquisition device also capable of measuring distances by correlating a set of images acquired under different parallaxes. Its single lens and single sensor architecture have two downsides: limited resolution and limited depth sensitivity. As a first step and in order to circumvent those shortcomings, we investigated how the basic design parameters of a plenoptic camera optimize both the resolution of each view and its depth-measuring capability. In a second step, we built a prototype based on a very high resolution Red One® movie camera with an external plenoptic adapter and a relay lens. The prototype delivered five video views of 820 × 410. The main limitation in our prototype is view crosstalk due to optical aberrations that reduce the depth accuracy performance. We simulated some limiting optical aberrations and predicted their impact on the performance of the camera. In addition, we developed adjustment protocols based on a simple pattern and analysis of programs that investigated the view mapping and amount of parallax crosstalk on the sensor on a pixel basis. The results of these developments enabled us to adjust the lenslet array with a submicrometer precision and to mark the pixels of the sensor where the views do not register properly.

Objective assessment of visual comfort for stereoscopic video is of great importance for stereoscopic image safety issue. We propose a novel visual comfort assessment metric framework that systematically exploits human visual attention models. In a stereoscopic video shot, perceptually significant regions where human subjects pay more attention are likely to play an essential role in determining the overall level of visual comfort. As a specific example of this concept, we develop a visual comfort metric that quantifies the level of visual discomfort caused by fast salient object motion. The performance of the proposed visual comfort metric has been evaluated using natural stereoscopic videos. The experimental results show that the proposed visual comfort metric significantly improves the correlations with subjective judgment.

Unlike the real world, stereoscopic cinemas and display devices have a bordered frame, which can unnaturally cut off our view, and create conflicting visual cues. These perceptual conflicts can diminish the three-dimensional (3-D) effect and cause visual fatigue for the audience. Presented is a method to resolve these issues, by developing a controllable articulated 3-D border.

We examined the image quality, image clarity, and viewing comfort of 2-D images rendered on an autostereoscopic display. Opinions on daily use of the content was gathered. Two different stereo displays were used in the experiments-a lenticular lens stereo display employing fixed 3-D stereo and a display with a switchable lenticular lens structure. Images were rendered on the displays with three different rendering schemes. Photos of natural scenes, artificial content, and content containing textual elements were used as the test stimuli. When images with natural scenes or artificial content were categorized into clusters according to the amount of details, significant differences in image quality, image clarity, and viewing comfort scores were observed. When two of the schemes were compared using the images containing textual elements, a significant difference in the viewing comfort and a significant increase in perceived stereoscopic depth impression were found with one of the schemes. Furthermore, image quality and viewing comfort were better with the 2-D display mode than with the 3-D mode. The use of the 2-D text content in the 3-D display mode seemed to be acceptable in general, but for longer term and repeated use, improvements in text quality should be considered. The results indicate that an increase in detail levels may decrease the evaluated image quality, clarity, and viewing comfort. Moreover, for all experimental conditions, better image quality, increased image clarity, and a more comfortable viewing experience had a positive influence on decisions of daily use.

This study presents a geometric and subjective analysis of typical mobile stereoscopic 3-D images. The geometry of the stereoscopic pipeline from the scene to the eyes of the viewer is a highly relevant issue in stereoscopic media. One important factor is camera separation, because it can be used to control the perceived depth of stereoscopic images. The geometric analysis included consideration of disparity and roundness factor within typical mobile stereoscopic imaging scenes. These geometric properties of stereoscopic 3-D images were compared to subjective evaluations by varying camera separation in different scenes. The participants in this study evaluated the strength and naturalness of depth sensation and the overall viewing experience from still images with the single-stimulus method. The results showed that participants were able to perceive the change of depth range even though the images were shown in random order without a reference depth scale. The highest naturalness of depth sensation and viewing experience were achieved with 2 cm to 6 cm camera separation in every content. With these preferred camera separations, the disparity range was less than ±1 deg⁡ and cardboard effect (quantified with roundness factor) did not negatively affect the naturalness of depth sensation.

We propose a novel method that improves watermark detection performance, based on suprathreshold stochastic resonance (SSR) with a maximizing network. The detection performance is measured in terms of correlation. The proposed method has been tested on different gray-scale images, and we found that an original image is not required for watermark detection, so this method is blind. We improved the correlation between an original watermark and the SSR with maximizing network-based discrete wavelet transform coefficients of the watermarked image. Our experimental results have been compared with the different existing techniques and were found superior in terms of correlation and ratio of correlation to threshold.

We propose an improved similarity measure (ISM) and apply it to the existing graph embedding (GE) framework to derive a new improved similarity measure-based graph embedding (ISM-GE) method for face recognition. Our work is motivated by the fact that both the Euclidean metric and the correlation metric are useful and effective for characterizing the similarity of face samples, and we combine these two metrics to form a new ISM to measure the similarity of face samples. We further utilize the proposed ISM in the existing GE framework and develop a new ISM-GE method for face feature extraction and recognition. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.

Watermarking is a technology for embedding secure information in digital content such as audio, images, and video. An effective watermarking algorithm is proposed based on a discrete wavelet transform (DWT) using mixed scales representation. The watermark is embedded in dominant blocks using quantization index modulation (QIM). These dominant blocks correspond to the texture and contour zones. Experimental results demonstrate that the proposed method is robust against various attacks and improves watermark invisibility.

Iris recognition utilizes distinct patterns found in the human iris to perform identification. Image acquisition is a critical first step toward successful operation of iris recognition systems. However, the quality of iris images required by standard iris recognition algorithms puts stringent constraints on the imaging systems, which results in a constrained capture volume. We have incorporated adaptive optical elements to expand the capture volume of a 3-m stand-off iris recognition system.

We present a novel matching method to find the correspondences among different images containing the same object. In the proposed method, by considering each feature point-set as a matrix, two point-sets are projected onto a common subspace using modified projective nonnegative matrix factorization. The core idea of the proposed approach is to jointly factorize of the two feature matrices and the matching operate on embeddings of the two point-sets in the common subspace. Furthermore, it is robust to noise due to the merit of the subspace method. The proposed approach was tested for matching accuracy, and robustness to noise. Its performance on synthetic and real images was compared with state-of-the-art reference algorithms.

In this paper, we propose an image compression algorithm that uses a hybrid transform and an improved modified set partitioning in hierarchical trees (SPIHT) coding algorithm. The proposed transform uses the subband discrete cosine transform to decompose the image into multiresolution subbands where the discrete wavelet transform is then used to code the low frequencies. Then, we use the SPIHT coding method to code the transformed coefficients. For the SPIHT algorithm, we have proposed a method to reduce the distortion introduced by the SPIHT technique between the original and reconstructed images. The obtained results show the efficiency of the proposed hybrid method in terms of peak signal-to-noise ratio and visual quality.

In this paper, a semiautomated system for modeling 3D objects, especially buildings from aerial video, over a semi-urban scene is presented. First, the video frames are preprocessed to minimize the rotational effects of camera motion. The 3D translational coordinates of the sensor are used to stitch the video frames into nadir and stereo mosaics. The features extracted from the stereo mosaics, like elevation, edges and corners, visual entropy, and color information, are employed in a Bayesian framework to identify the 3D objects in the scene, such as buildings and trees. The initial 3D building models are further optimized by projecting them onto individual video frames. A novel method for setting the input parameters of vision algorithms required for feature extraction, using the data-driven probabilistic inference in Bayesian Networks, has been designed. This method automates the 3D object identification process and precludes the need for manual intervention. Improvements that can be used to increase the accuracy of 3D models when Lidar data is fused with aerial video during the object identification process are also discussed.

Anisotropic diffusions are classified by the second eigenvalue of the Hessian matrix associated with the diffusivity function into two categories: one incapable of edge-sharpening and the other capable of selective edge-sharpening. A third class is proposed: the eigenvalue starts with a small value and decreases monotonically with image gradient magnitude, so that the stronger the edge is, the more it is sharpened. Two families of such diffusivity functions are proposed. Numerical simulations indicate that the noise removal performance of anisotropic diffusion does not correlate with the shape of the diffusivity function, but is, instead, determined by the shape of the second eigenvalue function. Diffusivity functions in the third category produce the best maximum peak signal-to-noise ratio in numerical simulations.

Active contour is one of the most successful variational models in image segmentation, pattern analysis, and computer vision. However, traditional active contour models not only require much expensive computation but are very sensitive to noise. We propose a scheme for noisy image segmentation integrating the active contour model with the contourlet transform, an optimal sparse representation of an image. Having reconstructed all the scale maps, we downsample the last but one scale map twice. Then, we apply the active contour model on the coarsest scale map and take the segmentation results as the initial curves for the finer scale map. Experiments have demonstrated that our proposed method can yield desired segmentation results both in real and synthetic images.

A high-speed vision system can be applied to color-histogram-based tracking at 2000 fps by hardware-implementing an improved CamShift algorithm. In the improved CamShift algorithm, the size, position, and orientation of a color-patterned object to be tracked in an image can be simultaneously extracted using only the hardware implementation of a color-histogram circuit module for calculating moment features of binary images quantized by 16 hue-based color bins. By hardware-implementing color-histogram circuit modules on a high-speed vision platform, IDP Express, the improved CamShift algorithm enables color-histogram-based tracking at 2000 fps for 512×511 pixel images in real-time. By installing our tracking system on a two-axis mechanical active vision system, we demonstrate the effectiveness of 2000 fps color-histogram-based tracking by performing several experiments of color-patterned objects, which are always tracked in the camera views even when they move rapidly under complicated backgrounds.

Distributed video coding (DVC) is an emerging video coding paradigm for systems that require low-complexity encoders that are supported by high-complexity decoders as required, for example, in real-time video capture and streaming from one mobile phone to another. Under the assumption of an error-free transmission channel, the coding efficiency of current DVC systems is still below that of the latest video codecs, such as H.264/AVC. In order to increase the coding efficiency, we propose that every Wyner-Ziv frame be downsampled by a factor of two prior to encoding and the subsequent transmission. However, this would necessitate upsampling in conjunction with interpolation at the decoder. Simple interpolation (e.g., a bilinear or bicubic filter) would be insufficient because the high-frequency (HF) spatial image content would be missing. Instead, we propose the incorporation of a super-resolution (SR) technique based upon the example-based scene-specific method to allow this HF content to be recovered. The SR technique will add computational complexity to the decoder side of the DVC system, which is allowable within the DVC framework. Rate-distortion curves show that this novel combination of SR and DVC improves the system's peak signal-to-noise ratio (PSNR) performance by up to several decibels and can actually exceed the performance of the H.264/AVC codec when GOP = IP for some video sequences.

A novel saliency model is proposed in this paper to automatically process images in the similar way as the human visual system which focuses on conspicuous regions that catch human beings' attention. The model combines a hypergraph representation and a partitioning process with potential region-of-interest (p-ROI) approximation and validation. Experimental results demonstrate that the proposed method shows considerable improvement in the performance of saliency map generation.

Rate control is a critical issue in H.264/AVC video coding standard because it suffers from some shortcomings that make the bit allocation process not optimal. This leads to a video quality that may vary significantly from frame to frame. Our aim is to enhance the rate control efficiency in H.264/AVC baseline profile by handling two of its defects: the initial quantization parameter (QP) estimation for Intra-Frames (I-Frames) and the target number of bits determination for Inter-Frames (P-Frames) encoding. First, we propose a Rate-Quantization (R-Q) model for the I-Frame constructed empirically after extensive experiments. The optimal initial QP calculation is based on both target bit-rate and I-Frame complexity. The I-Frame target bit-rate is derived from the global target bit-rate by using a new non-linear model. Secondly, we propose an enhancement of the bit allocation process by exploiting frame complexity measures. The target number of bits determination for P-Frames is adjusted by combining two temporal measures: the first is a motion ratio based on actual bits used to encode previous frames; the second measure exploits the difference between two consecutive frames and the histogram of this difference. The simulation results, carried out using the JM15.0 reference software and the JVT-O016 rate control algorithm, show that the right choice of initial QP for I-Frame and first P-Frame allows improvement of both the bit-rate and peak signal-to-noise ratio (PSNR). Finally, the Inter-Frame bit allocation process further improves the bit-rates while keeping the same PSNR improvement (up to +1.33  dB/+2  dB for QCIF/CIF resolutions). Moreover, this process reduces the buffer level variation leading to a more consistent quality of reconstructed videos.

Chaos maps and chaotic systems have been proved to be useful and effective for cryptography. In our study, the two-dimensional logistic map with complicated basin structures and attractors are first used for image encryption. The proposed method adopts the classic framework of the permutation-substitution network in cryptography and thus ensures both confusion and diffusion properties for a secure cipher. The proposed method is able to encrypt an intelligible image into a random-like one from the statistical point of view and the human visual system point of view. Extensive simulation results using test images from the USC-SIPI image database demonstrate the effectiveness and robustness of the proposed method. Security analysis results of using both the conventional and the most recent tests show that the encryption quality of the proposed method reaches or excels the current state-of-the-art methods. Similar encryption ideas can be applied to digital data in other formats (e.g., digital audio and video). We also publish the cipher MATLAB open-source-code under the web page https://sites.google.com/site/tuftsyuewu/source-code.

This paper presents a fast multiphase segmentation model for inhomogeneous images by incorporating the multiphase formulation of the local and global intensity fitting energy model and the split Bregman method. By applying the globally convex image segmentation idea, we first define a new energy functional, which is then modified by incorporating information from the edge. A weight function that varies dynamically with the location of the image is applied to balance the weights between the local and global intensity fitting terms. The split Bregman method is then applied to minimize our energy functional much more efficiently. Our model can segment more general images accurately, especially images with inhomogeneity. We have applied our model to synthetic and real inhomogeneous images with desirable results. Numerical results demonstrate that our model is superior to the piecewise constant multiphase models and the local binary fitting model. The results obtained by our model are even more accurate than the original local and global intensity fitting energy model.

We present a new energy-based compression model for the display of high dynamic range images. The proposed tone mapping method tends to exploit the biologically inspired dynamic retina concept, which is herein mathematically expressed via an image representation based on the specification of the statistical distributions of the nonlocal gradient magnitude. In this framework, which also operates the notion of nonlocal gradient recently put forward by Gilboa and Osher, the detail-preserving contrast reduction problem is therefore expressed by a energy-based model with nonlocal pairwise pixel interactions defined on a complete graph whose cost function is locally minimized by a conjugate gradient descent procedure. The experiments demonstrate that the proposed compression method is efficient and provides pleasing results on various images with different scene contents and performs competitively compared to the best existing state-of-the-art tone mapping methods recently proposed in the literature.

Hexagons are widely observed as a packing or tiling geometry in nature, yet they appear to have been avoided in conventional halftone tiling. A goal of the present study is to understand the potential barriers that have prevented their use and present new halftone geometry options that overcome the issues while offering several potential benefits. While conventional halftone geometries often include the fourth screen (e.g., yellow) in a suboptimal manner, the hexagonal geometry presented here can include a clustered-dot fourth screen moiré-free. Hexagonal screens can appear to have smoother texture. Due to differences in packing geometry and touch point geometry, hexagons have the potential to possess different tone reproduction characteristics, which may be favorable for some marking processes. We also present a parametrically controlled hexagonal halftone spot function that allows for optimization of dot touch points and provides compact growth. The controllable touch points can prevent a tone reproduction discontinuity, while the compact growth throughout the gray range ensures maximum stability. Additionally, we present a three-colorant dot-off-dot halftone configuration using hexagonal geometry. Examples are provided.

The application of hyperspectral sensors in the development of machine vision solutions has become increasingly popular as the spectral characteristics of the imaged materials are better modeled in the hyperspectral domain than in the standard trichromatic red, green, blue data. While there is no doubt that the availability of detailed spectral information is opportune as it opens the possibility to construct robust image descriptors, it also raises a substantial challenge when this high-dimensional data is used in the development of real-time machine vision systems. To alleviate the computational demand, often decorrelation techniques are commonly applied prior to feature extraction. While this approach has reduced to some extent the size of the spectral descriptor, data decorrelation alone proved insufficient in attaining real-time classification. This fact is particularly apparent when pixel-wise image descriptors are not sufficiently robust to model the spectral characteristics of the imaged materials, a case when the spatial information (or textural properties) also has to be included in the classification process. The integration of spectral and spatial information entails a substantial computational cost, and as a result the prospects of real-time operation for the developed machine vision system are compromised. To answer this requirement, in this paper we have reengineered the approach behind the integration of the spectral and spatial information in the material classification process to allow the real-time sorting of the nonferrous fractions that are contained in the waste of electric and electronic equipment scrap.

A novel noise detector based on the spiking cortical model (SCM) is proposed for switching-based filters. In the proposed noise detector, the corrupted pixels are firstly identified as noise candidates based on the firing time of the SCM, and then the misclassified noise-free pixels are dismissed from noise candidates based on the absolute difference of the firing time between the considered neurons and their neighboring neurons. Extensive simulations show that although the proposed noise detector generally has lower computational efficiency than several state-of-the-art noise detectors, it outperforms all the compared noise detectors in noise detection accuracy by classifying the pixels in the corrupted images with very few or no mistakes at the various noise ratios.

We comparatively evaluated two types of image-reconstruction algorithms in two types of single-shot phase-shifting digital holography techniques to investigate which algorithm is more suitable for high-quality and instantaneous three-dimensional imaging. Two types of image-reconstruction algorithms were proposed for each single-shot phase-shifting interferometry so far. One generates multiple phase-shifted holograms from the recorded single hologram by utilizing interpolation and derives the complex amplitude of the object wave by applying the calculation of phase-shifting interferometry to the multiple phase-shifted holograms. The other does not use interpolation but applies the calculation of phase-shifting interferometry to the neighboring pixels in the recorded hologram to derive the complex amplitude. We experimentally evaluated the qualities of the image reconstructed by the algorithms in each single-shot phase-shifting technique. It was clarified that the former algorithm is more effective for high-quality imaging free from the unwanted images in single-shot technique using an array of optical elements. In single-shot technique using spatial carrier, although the latter algorithm is able to reconstruct a brighter image for wide area than the former, spatial-filtering technique is needed for removing the conjugate image.

Steganographic methods are desired to have the least impact on the statistical and perceptual properties of the cover image to prevent steganalytical methods from the detection of data hiding in the image. The existing spatial domain methods of steganography generally have low data-hiding capacity and low security. We present an adaptive spatial domain steganographic method with increased security and capacity. The method partitions the image into flat and nonflat blocks and uses different embedding strategies based on the block type. These strategies are based on modifying certain pixels within each 3×3 block of the image. Experimental results show that the histogram and higher order statistics-based steganalytical methods cannot detect the stego images produced by the proposed method and that the capacity is increased significantly.

Given an image of a rough surface texture, the approach described here can estimate the direction from which the surface was lit. Unlike previous work, we require neither surface isotropy nor that the texture is included in the training set. The approach is based on active basis and the Mojette transform. The Mojette transform is used to estimate the orientation of the texture; it does so by finding which training samples have similar orientation features. The active basis model is then learned from training images by the shared pursuit algorithm. Next, the base histograms of the test image and textures with similar orientation features in the training set are compared so that the illumination directions can be estimated by minimizing their correlation coefficients.

The Yule-Nielsen modified spectral Neugebauer model (YNSN) enables predicting reflectance spectra from ink surface coverages of halftones. To provide an improved prediction accuracy, this model is enhanced with an ink spreading model accounting for ink spreading in all superposition conditions (ISYNSN). As any other spectral reflection prediction model, the ISYNSN model is conceived to predict the reflection spectra of color-constant patches. Instead of color-constant patches, we investigate if tiles located within color images can be accurately predicted and how they can be used to facilitate the calibration of the ink spreading model. We detail an algorithm to automatically select image tiles as uniform as possible from color images by relying on their CMY or CMYK pixel values. The tile selection algorithm incorporates additional constraints relying on surface coverages of the inks. We demonstrate that an ink spreading model calibrated with as few as 5 to 10 optimally chosen image tiles allows the corresponding YNSN model to provide accurate spectral predictions.

A novel method is proposed for removing impulse (random-valued and salt-and-pepper) noise from multichannel digital images based on an adapted version of the DEEPLOC algorithm introduced by A. Struyf and P. J. Rousseeuw for calculation of approximate half-space (Tukey's) deepest location (median) in multivariate case. Due to its intrinsic multivariate/multidimensional nature, the proposed method eliminates the noise simultaneously on all channels without their separation, which preserves the spectral correlation between channels in a multichannel image. Denoising results of this new nonlinear spatial domain filter applied to benchmark images outperform currently used state-of-the-art filters for impulse noise removal from multichannel images in terms of both objective effectiveness criteria [peak-signal-to-noise-ratio (PSNR), mean absolute error (MAE), and normalized color distance (NCD)] and visual quality. The proposed filter successfully preserves the edges and fine image details, and is very effective for removal of medium and heavy multichannel impulse noise.

A book review of a recently released title.