Visual applications depend on image quality for algorithmic decision-making, and atmospheric conditions such as smoke and haze produce a challenge to artificial systems that rely on identification of people, objects, and obstacles. Smoke is particularly difficult because of its nonhomogeneous characteristics and irregular image coverage. Nighttime images worsen the problem because of low light and artificial light conditions. Our aim was to develop an iterative process that removes smoke from images in daytime and nighttime scenes. The haze image model was used as our baseline model. First, we developed a detection method to find the smoky regions on the image and used the dark channel haze removal process to estimate the transmission map for each color channel. We ran the algorithm iteratively because a one-time process left residual smoke. Blue smoke produced an unbalanced particle density, so the blue color channel had to be corrected more times than red and green channels. Finally, we optimized the image in postprocessing, and the results produced smoke-free images. We believe our algorithm is the first to successfully remove nighttime smoke.
Empirical mode decomposition (EMD) is a simple, local, adaptive, and efficient method for nonlinear and nonstationary signal analysis. However, for dealing with multidimensional signals, EMD and its variants such as bidimensional EMD (BEMD) and multidimensional EMD (MEMD) are very slow due to the needs of a large amount of envelope interpolations. Recently, a method called iterative filtering has been proposed. This filtering-based method is not as precise as EMD but its processing speed is very fast and can achieve comparable results as EMD does in many image and signal processing applications. We combine quaternion algebra and iterative filtering to achieve the edge detection, color quantization, segmentation, texture removal, and noise reduction task of color images. We can obtain similar results by using quaternion combined with EMD; however, as mentioned before, EMD is slow and cumbersome. Therefore, we propose to use quaternion iterative filtering as an alternative method for quaternion EMD (QEMD). The edge of color images can be detected by using intrinsic mode functions (IMFs) and the color quantization results can be obtained from residual image. The noise reduction algorithm of our method can be used to deal with Gaussian, salt-and-pepper, speckle noise, etc. The peak signal-to-noise ratio results are satisfactory and the processing speed is also very fast. Since textures in a color image are high-frequency components, we also can use quaternion iterative filtering to decompose a color image into many high- and low-frequency IMFs and remove textures by eliminating high-frequency IMFs.
Uneven light distribution problems often arise in poorly scanned text or text-photo images and natural images taken by digital camera. An innovative image-processing technique for uneven illumination removal using empirical mode decomposition (EMD) is proposed. The EMD is local, adaptive, and useful for analyzing nonlinear and nonstationary signals. In this method, we decompose images by EMD and get the background level locally and adaptively. This algorithm can enhance the local reflectance in the image while removing uneven illumination for black/white text images, text-photo images, and natural color/gray-level images. The proposed technique can be very helpful for image and text recognition. The EMD can also be applied to the three color channels (RGB) of color images separately to estimate the reflectances of the three color channels. After we relight these channels using white light and the estimated reflectances, a simple color constancy task can be performed to correct certain poorly lighted color images. Our technique is compared with recently proposed methods for correcting images with uneven illumination and the experimental results demonstrated that the proposed approach can effectively enhance natural color/gray-level images and make text and text-photo images more readable under uneven illumination.
Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud
computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is
envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the
fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first
to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all
the operations in SIFT must be moved to the encrypted domain, we propose a homomorphic encryption-based
secure SIFT method for privacy-preserving feature extraction and representation based on Paillier cryptosystem.
In particular, homomorphic comparison is a must for SIFT feature detection but is still a challenging issue for
homomorphic encryption methods. To conquer this problem, we investigate a quantization-like secure comparison
strategy in this paper. Experimental results demonstrate that the proposed homomorphic encryption-based SIFT
performs comparably to original SIFT on image benchmarks, while preserving privacy additionally. We believe
that this work is an important step toward privacy-preserving multimedia retrieval in an environment, where
privacy is a major concern.
Proc. SPIE. 7744, Visual Communications and Image Processing 2010
KEYWORDS: Digital signal processing, Lithium, Image compression, Image processing, Fourier transforms, Image analysis, Signal analysis, Visual communications, Communication engineering, Current controlled current source
It is known that the 2-D DCT basis is complete and orthogonal in a rectangular region. In this paper, we introduce the
way to generate the complete and orthogonal 2-D DCT basis in a trapezoid region or a triangular region without using
the complicated Gram-Schmidt method. Moreover, since a polygon can be decomposed several triangular regions, the
proposed method is also suitable for the polygonal region. Our algorithm can much generalize the JPEG algorithm.
Instead of dividing an image into 8 by 8 blocks, we can divide an image into trapezoid or triangular regions and then
transform and code each of them. In addition to the DCT basis, our method can also be used for generating the 2-D
complete and orthogonal DFT basis, KLT basis, Legendre basis, Hadamard (Walsh) basis, and polynomial basis in the
trapezoid and triangular regions.
Various watermarking schemes were developed in an attempt to address the piracy issue. One of the most important requisites for an effective watermarking scheme is its robustness. A robust watermarking scheme means the embedded watermark can still be extracted successfully from an attacked watermarked data. Regardless of the motivations, the attacked watermarked data should have an acceptable quality, that is, an attacker can't remove the embedded watermark without penalty. In this paper, we propose the robustness can be improved by mingling a reference signal during watermark embedding. The knowledge of reference signal at receiver end makes better channel estimation and lower probability of detection error. To show the performance improved by mingling reference watermark, we applied it to quantization-based image watermarking in discrete cosine transform (DCT) domain and watermarked image was attacked by JPEG compression. The simulation results will show mingling reference signals really achieved better performance. Having the same quality of attacked watermarked image, probability of error with reference signals mingling could be lower than without it.
Proc. SPIE. 2501, Visual Communications and Image Processing '95
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Edge detection, Detection and tracking algorithms, Sensors, Image processing, Computer simulations, Colorimetry, Signal processing, Smoothing
Chromatic boundaries are edges caused by material changes. To detect these boundaries in the color images, chromatic information must be used. In this paper, we present an integration algorithm to combine adaptive smoothing technique and vector gradient approach together. Simulation results show that the integration algorithm earns the benefits of both adaptive smoothing technique and vector gradient approach. It can detect chromatic boundaries effectively and smooth out the variations within chromatic boundaries. Whether input color images are contaminated noise or not, the integration algorithm performs better than both adaptive smoothing for intensity image and vector gradient approach.
We describe a complex log mapping approach for 2-D color object recognition. First, a 3-D vector color image is transformed into a 2-D vector image by projection onto a color plane, then a complex log mapping transform is applied to this 2-D vector color image for invariant pattern recognition. Using extensive computer simulations, this method can effectively recognize a scale- and rotation-changed color object among objects of similar shape but with different color contents and also among objects of different shape and color.
An efficient subband image decomposition method for monochrome and color images made possible by mathematical morphology is described. The input signal spectrum is decomposed into four-subband and two-subband images by using two different sets of structuring elements. Then each band image can be decimated and coded effectively for data transmission. This subband pyramid scheme preserves the number of pixels as that in the original image Also, the data structure itself is very compact. The advantages of morphology over the linear filtering approach are its direct geometric interpretation, simplicity, and efficiency in hardware implementation. Some image examples are presented to show the effectiveness of this approach.
This paper describes a complex log mapping approach to 2D color object recognition. First a 3D vector color image is first transformed into a 2D vector image by projection into a color plane, then complex log mapping transform is applied on this 2D vector color image for invariant pattern recognition; By extensive computer simulations, this method can effectively recognize a scale and rotation changed color object among the similar shape objects but with different colors, and also among the different shape objects as well very successfully.
The morphological pyramid is a new computationally efficient algorithm for generating multidimensional bandpass representation of an image. This image pyramid offers a flexible convenient multiresolution format that mirrors the multiple scales of processing in the human visual system. Here the morphological pyramid is used for image mosaic and smear removal by multiresolution interpolation, some image examples are illustrated to show the effectiveness of this approach.
An efficient subband image decomposition method by mathematical morphology is described in this paper, it decomposes the input signal spectrum into 4 subband images by using two separable structure elements, then each band image, can be decimated and coded effectively for data transmission. This subband pyramid scheme preserves the number of pixels as in the original image, also the data structure itself is very compact, the advantages of morphology over linear filtering approach are its direct geometric interpretations, simplicity and efficiency in implementation, some image examples are given to show the effectiveness of this approach.