“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

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Combination of Evidence in Dempster-Shafer Theory

A general framework for image fusion based on multi-scale transform and sparse representation

Anthropometry of the head and face

To obtain an image with every object in focus, we always need to fuse images taken from the same view point with different focal settings. Multiresolution transforms, such as pyramid decomposition and wavelet, are usually used to solve this problem. In this paper, a sparse representation-based multifocus image fusion method is proposed. In the method, first, the source image is represented with sparse coefficients using an overcomplete dictionary. Second, the coefficients are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. Furthermore, the proposed fusion scheme can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm. The proposed method is compared with spatial gradient (SG)-, morphological wavelet transform (MWT)-, discrete wavelet transform (DWT)-, stationary wavelet transform (SWT)-, curvelet transform (CVT)-, and nonsubsampling contourlet transform (NSCT)-based methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

Multifocus Image Fusion and Restoration With Sparse Representation

Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. We propose to filter this noise with a multiresolution analysis of the image. The wavelet coefficient of the reflectivity is estimated with a Bayesian model, maximizing the a posteriori probability density function. The different probability density function are modeled with the Pearson system of distributions. The resulting filter combines the classical adaptive approach with wavelet decomposition where the local variance of high-frequency images is used in order to segment and filter wavelet coefficients.

Multiscale MAP filtering of SAR images

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

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Convolutional Neural Networks for the Automatic Identification of Plant Diseases.

Speckle noise filtering on polarimetric SAR (PolSAR) images remains a challenging task due to the difficulty to reduce a scatterer-dependent noise while preserving the polarimetric information and the spatial information. This challenge is particularly acute on single look complex images, where little information about the scattering process can be derived from a rank-1 covariance matrix. This paper proposes to analyze and to evaluate the performances of a set of PolSAR speckle filters. The filter performances are measured by a set of ten different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, point target, and edge preservation. The result is a performance profile for each individual filter. The methodology consists of simulating a set of artificial PolSAR images on which the various filters will be evaluated. The image morphology is stochastic and determined by a Markov random field and the number of scattering classes is allowed to vary so that we can explore a large range of image configurations. Evaluation on real PolSAR images is also considered. Results show that filters performances need to be assessed using a complete set of indicators, including distributed scatterer parameters, radiometric parameters, and spatial information preservation.

Analysis, Evaluation, and Comparison of Polarimetric SAR Speckle Filtering Techniques

Presents a pixel-level fusion method to refine the resolution of a multi-spectral image using a high-resolution panchromatic image. Our approach is an adaptation of the ARSIS method which takes into account the higher-order statistical moments of the wavelet coefficients and allow processing of non-dyadic images.

Multi- spectral image resolution refinement using stationary wavelet transform

In the last decade there has been a growing interest in the study of sparse representation of signals. In particular, many new multiscale image representations in a geometric space have been proposed (Curvelets, Ridgelets, Contourlets, etc.). Instead of using a fixed transformation, an alternative approach is to build a sparse dictionary from the signal itself. In the present work, we propose a novel approach for speckle noise reduction in SAR images using a sparse and redundant representation over trained dictionaries. In this approach, an adaptive dictionary composed of image patches (called atoms) is learned from the image so that it constitutes a sparse representation of the image content. This learning process, called K-SVD, is efficiently performed using an Orthogonal Matching Pursuit (OMP) and a Singular Value Decomposition (SVD). This new approach is effective in removing white additive Gaussian noise despite the fact that elements of the dictionary are learned from the noisy image, the algorithm is converging toward meaningful atoms that are already showing a reduction in noise level.

Samuel Foucher

Papers

Multiscale MAP filtering of SAR images

Convolutional Neural Networks for the Automatic Identification of Plant Diseases.

Analysis, Evaluation, and Comparison of Polarimetric SAR Speckle Filtering Techniques

Multi- spectral image resolution refinement using stationary wavelet transform

SAR Image Filtering Via Learned Dictionaries and Sparse Representations