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Mutual Information Loss in Pyramidal image processing

INFORMATION 2020年第6期11卷 322页

作者： Gibson, Jerry Oh, Hoontaek Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

Gaussian and Laplacian pyramids have long been important for image analysis and compression. More recently, multiresolution pyramids have become an important component of machine learning and deep learning for image analysis and image recognition. Constructing Gaussian and Laplacian pyramids consists of a series of filtering, decimation, and differencing operations, and the quality indicator is usually mean squared reconstruction error in comparison to the original image. We present a new characterization of the information loss in a Gaussian pyramid in terms of the change in mutual information. More specifically, we show that one half the log ratio of entropy powers between two stages in a Gaussian pyramid is equal to the difference in mutual information between these two stages. We show that this relationship holds for a wide variety of probability distributions and present several examples of analyzing Gaussian and Laplacian pyramids for different images.

关键词： Gaussian pyramids Laplacian pyramids pyramidal image processing multiresolution image processing log ratio of entropy powers

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Product-based multiresolution image fusion

Product-based multiresolution image fusion

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Conference on Multisensor, Multisource Information Fusion

作者： Ostrem, T Kozaitis, SP Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)0819457981

We combined images from different sensors that were enhanced by multiscale products of the wavelet coefficients. Using the wavelet transform, we used a multiresolution analysis to form products of coefficients across scales. Then, a fusion rule was applied to the product images to determine how the original images could be combined. Using this approach, we were able to decrease the sensitivity of the fusion process to noise.

关键词： multiresolution image processing sensor fusion wavelet transform

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Disparity determination using multilevel products

Disparity determination using multilevel products

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Conference on Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III

作者： Ouendeno, M Kozaitis, SP Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)0819458031

We determined the overall disparity from stereo images using multiscale products of disparity values. Using the wavelet transform, we used a multiresolution analysis to calculate disparity values from stereo images at different scales. Forming the product of disparities and rescaling resulted in a disparity map that was directly related to depth. Using a multiresolution approach allowed us to more accurately determine disparity by examining the consistency of results through different scales. Using this approach could form the basis of an effective method for depth estimation.

关键词： disparity multiresolution image processing stereo vision wavelet transform

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Multilevel fusion using enhanced feature detection

Multilevel fusion using enhanced feature detection

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Conference on Visual Information processing XIV

作者： Ostrem, T Kozaitis, SP Laszlo, I Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)0819458023

We combined images from different sensors based on the magnitude of multiscale products of the wavelet transform. Using the product of nondownsampled wavelet coefficients across scales, we formed a fusion rule between two images. In this way, the products represented features than were related to contrast. Therefore, using our approach allowed us to enhance the contrast of two images using a pixel-level fusion approach. In experiments, our approach compared favorably to using only a single level, or other methods using subjective tests.

关键词： multiresolution image processing sensor fusion wavelet transform

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Gradient-based multiresolution image fusion

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IEEE TRANSACTIONS ON image processing 2004年第2期13卷 228-237页

作者： Petrovic, VS Xydeas, CS Univ Manchester Dept Comp Sci Imaging Sci Biomed Engn Grp Manchester M13 9PT Lancs England Univ Lancaster Dept Commun Syst Lancaster LA1 4YR England

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.

关键词： gradient image representation image fusion information fusion multiresolution image processing

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A VISUAL-CORTEX DOMAIN MODEL AND ITS USE FOR VISUAL INFORMATION-processing

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NEURAL NETWORKS 1991年第1期4卷 3-13页

作者： RYBAK, IA SHEVTSOVA, NA PODLADCHIKOVA, LN GOLOVAN, AV Institute of Neurocybernetics at Rostov State University USSR

A model of an iso-orientation domain in the visual cortex is developed. The iso-orientation domain is represented as a neural network with retinotopically organized afferent inputs and anisotropic lateral inhibition formed by feedbacks via inhibitory interneurons. Temporal dynamics of neuron responses to oriented stimuli is studied. The results of computer simulations are compared with those of neurophysiological experiments. It is shown that the later phase of neuron response has a more sharp orientation tuning than the initial one. It is suggested that the initial phase of neuron responses encodes intensity parameters of visual stimulus, whereas the later phase encodes its orientation. The design of the neural network preprocessor and the architecture of the system for visual information processing, based on the idea of parallel-sequential processing, are proposed. The example of a test image processing is presented.

关键词： VISUAL CORTEX NEURAL NETWORK LATERAL INHIBITION ISO-ORIENTATION DOMAIN FEATURE EXTRACTION ORIENTATION FILTRATION multiresolution image processing

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