The restoration of vibration-blurred images using measured motion function is considered. Since the blurring filter is of the finite impulse response type, the inverse one is of the all-pole infinite impulse response type. Direct application of the inverse filter to restore images blurred by vibration is attractive because of reduced computation requirements. Space domain filtering allows high parallelization of the process and reduction of required processor speed. However, a pure inverse filter provides excessive noise amplification and is possibly unstable. The proposed technique is to construct a modified inverse filter with preserved all-pole structure and optimized noise and stability properties. A mathematical concept was developed using z-transform properties. An experiment testing the proposed technique was setup and the results indicate restoration is 60%–80% of that possible with ideal Wiener filtering. However, the reduced computation and high parallelization can facilitate real-time restoration.
This paper presents a new methodology for the reduction of sensor noise from images acquired using digital cameras at high-International Organization for Standardization (ISO) and long-exposure settings. The problem lies in the fact that the algorithm must deal with hardware-related noise that affects certain color channels more than others and is thus nonuniform over all color channels. A new adaptive center-weighted hybrid mean and median filter is formulated and used within a novel optimal-size windowing framework to reduce the effects of two types of sensor noise, namely blue-channel noise and JPEG blocking artifacts, common in high-ISO digital camera images. A third type of digital camera noise that affects long-exposure images and causes a type of sensor noise commonly known as "stuck-pixel" noise is dealt with by preprocessing the image with a new stuck-pixel prefilter formulation. Experimental results are presented with an analysis of the performance of the various filters in comparison with other standard noise reduction filters.
We describe a new method for noise suppression and edge enhancement in digital images based on the wavelet transform. At each resolution, the coefficients associated with noise are modeled by Gaussian random variables. Coefficients associated with edges are modeled by generalized Gaussian random variables, and a shrinkage function is assembled based on posterior probabilities. The shrinkage functions at consecutive scales are combined, and then applied to the wavelets coefficients. Finally, a diffusion equation is applied to the modified wavelet coefficients, to preserve edges that are not isolated. This method is adaptive to different amounts of noise in the image, and tends to be more robust to larger noise contamination than comparable techniques. Compared to a state of the art method that does not require the user to adjust parameters, as in our case, our method presents a superior performance.
Conventional digital halftoning approaches function by modulating either the dot size [amplitude modulation (AM)] or the dot density [frequency modulation (FM)]. Generally, AM halftoning methods have the advantage of low computation and good print stability, while FM halftoning methods typically have higher spatial resolution and resistance to moiré artifacts. In this paper, we present a new class of AM/FM halftoning algorithms that simultaneously modulate the dot size and density. The major advantages of AM/FM halftoning are better stability than FM methods through the formation of larger dot clusters; better moiré resistance than AM methods through irregular dot placement; and improved quality through systematic optimization of the dot size and dot density at each gray level. We present a general method for optimizing the AM/FM method for specific printers, and we apply this method to an electrophotographic printer using pulse width modulation technology.
This paper presents a combined halftoning and compression approach which achieves high quality halftones and compression ratios comparable to those of JBIG and JBIG2 with much simpler encoding and decoding schemes. We use tone-dependent error diffusion and a tree structure to efficiently develop the halftone texture from a limited set of binary one-dimensional tokens. Local estimation of tone further restricts the candidate set of tokens. In areas of the image containing edges or detail, the token is not constrained. A Huffman code is used to efficiently encode the tokens with minimal complexity. Experimental results show that visual quality is very close to that of regular tone-dependent error diffusion.
The development of plastic card printers has led to the widespread use of identity documents printed on plastic cards, such as ID cards, driving licenses, and access key cards. This paper presents a new security feature based on a technique for embedding a personalized microstructure into an image. This microstructure takes the form of a pattern embedded into the original photograph as a succession of balanced chromatic shifts. The amplitude of these shifts may be tuned so as to make the pattern fully apparent or just noticeable under normal viewing conditions. Since the chromatic shifts cancel each other out in any macroscopic portion of the image, the global appearance of the protected image remains intact. The embedded microstructure may be adapted to each instance of the protected identity document. For example, it can repeat textual information already present elsewhere on the document, or it can include a code derived from data specific to the document holder. Furthermore, this information may be made fully readable without requiring special revealing means. Such identity documents exhibit an intrinsic resistance against imitation, tampering and substitution.
Vector quantization (VQ) is an effective technology for signal compression. In traditional VQ, most of the computation concentrates on searching the nearest codeword in the codebook for each input vector. We propose a fast VQ algorithm to reduce the encoding time. There are two main parts in our proposed algorithm. One is the preprocessing process and the other is the practical encoding process. In preprocessing, we will generate some tables that we need to employ for practical encoding. Because those tables are used for all the images, the time to generate these tables does not increase any time in the practical encoding process. On the second part, the practical encoding process, we use the tables generated previously and other techniques to speed up the encoding time. This paper provides an effective algorithm to accelerate the encoding time. The proposed algorithm demonstrates the outstanding performance in terms of time saving and arithmetic operations. Compared to a full search algorithm, it saves more than 95% searching time.
We propose a fast partial decoding algorithm for content access to MPEG compressed videos, where full decompression is not necessarily required, such as compressed video browsing, content analysis, and specific pattern search. The proposed decoding bypasses the inverse DCT via an approximation process to extract average pixels directly from compressed DCT coefficients. Although such extracted pixels may incur differences compared with their fully decompressed counterparts, extensive experiments show that such partially decoded video frames preserve their content very well and achieve reasonable perceptual quality in terms of visual inspections.
The use of computational metrics to control and assess the visual quality of digital images is well known. This paper presents a quality metric including a visual channels representation and a new contrast masking model. Based on the measure of maximum quantization steps without visual impairments, the model considers both intrachannel and interchannel masking and is derived from extensive experiments conducted on noise and texture images instead of simple sinusoidal stimuli. The metric parameters are optimized in order to maximize the linear correlation coefficient as well as the Spearman rank-order correlation between the computed quality measures and the mean opinion score.
TOPICS: Image segmentation, Signal to noise ratio, Matrices, Monte Carlo methods, Sensors, Computer simulations, Data modeling, Image classification, Interference (communication), Expectation maximization algorithms
We consider the problem of the blind separation of noisy instantaneously mixed images. The images are modeled by hidden Markov fields with unknown parameters. Given the observed images, we give a Bayesian formulation and we propose a fast version of the MCMC (Monte Carlo Markov Chain) algorithm based on the Bartlett decomposition for the resulting data augmentation problem. We separate the unknown variables into two categories: 1. The parameters of interest which are the mixing matrix, the noise covariance and the parameters of the sources distributions. 2. The hidden variables which are the unobserved sources and the unobserved pixel segmentation labels. The proposed algorithm provides, in the stationary regime, samples drawn from the posterior distributions of all the variables involved in the problem leading to great flexibility in the cost function choice. Finally, we show the results for both synthetic and real data to illustrate the feasibility of the proposed solution.
There is an urgent need to extract key information automatically from video for the purposes of indexing, fast retrieval, and scene analysis. To support this vision, reliable scene change detection algorithms must be developed. This paper describes a novel algorithm for wipe scene change detection in uncompressed and MPEG-1 compressed video sequences using statistical and geometric properties of each image. An efficient algorithm is also proposed to estimate statistical features in compressed video without full frame decompression. From uncompressed and MPEG compressed frames, thumbnails are obtained where pixels are the averages and variances of luminance values of macroblocks. Differences between thumbnails are computed and thresholded, and straight lines are detected in the resulting binary images. Persistence and motion of these lines indicate the presence of a shot transition using wipes. Results on video of various content types are reported and validated with the proposed schemes. Furthermore, results show that the accuracy of the detection is above 95% for uncompressed and above 90% for MPEG-1 compressed video.
The parallel stereoscopic camera has a linear relationship between vergence and focus control. We introduced the automatic control method for a stereoscopic camera system that uses the relationship between vergence and focus of a parallel stereoscopic camera. The automatic control method uses disparity compensation of the acquired image pair from the stereoscopic camera. For faster extraction of disparity information, the proposed binocular disparity estimation method by the one-dimensional cepstral filter algorithm would be investigated. The suggested system in this study greatly reduces the extraction time requirement and error so as to offer spontaneous control and greater real-time realism to acquire high quality stereoscopic images.
We propose a method that uses projection models in conjunction with a sequential Monte Carlo approach to track rigid targets. We specifically address the problems associated with tracking objects in scenarios characterized by cluttered images and high variability in target scale. The projection model snake is introduced in order to track a target boundary over a variety of scales by geometrically transforming the boundary to account for three-dimensional relative motion between the target and camera. The complete solution is a potent synergism of the projection model snake and a sequential Monte Carlo method. The projection model Monte Carlo method randomly generates the parameters of target motion and pose from empirically derived distributions. The resultant "particles" are then weighted according to a likelihood determined by the integration of the mean gradient magnitude around the target contour, yielding the expected target path and pose. We demonstrate the effectiveness of this approach for tracking dynamic targets in sequences with noise, clutter, occlusion, and scale variability.