Hyperspectral imaging is a valuable tool for analyzing and understanding remote sensing data. Our research paper presents a pioneering method for hyperspectral unmixing through the utilization of a convolutional autoencoder (CAE). To train the CAE, our approach incorporates mutual channels loss (MCL) as a loss function, and we implement channel-wise attention within the CAE architecture. Furthermore, we conduct experiments using two datasets, namely the Samson and Apex hyperspectral datasets, to compare the outcomes of our approach against those achieved by state-of-the-art methods. Our findings demonstrate that our suggested approach achieves significant improvements in terms of accuracy, efficiency and being robust as compared to existing methods. These results highlight the potential of our approach for improving hyperspectral unmixing in a range of remote sensing applications.

This paper addresses the problem of depth estimation in light field images by handling occlusions in a robust way. Previous methods determine the occlusion maps either by using the edge information in the center view or by employing various cues based on disparity cost. Here we propose to determine the occlusions based on both disparity cost and edge information. The proposed method first gets a collective response from all the depth cues of the different views. Then it determines the occluded pixels by relying on the edge information in this collective response. Based on this predicted occlusion map, the resulting depth map is regularized by standard graph-cut optimization and filtered using weighted median filtering. Experimental results on synthetic light field images demonstrate the superiority of the proposed method compared to the state-of-the-art both in quantitatively and qualitatively.

In this paper, we propose a novel multiple pyramids based image inpainting method using local patch statistics and geometric feature-based sparse representation to maintain texture consistency and structure coherence. First, we approximate each patch in the target region (region to be inpainted) by statistically dominant local candidate patches to preserve local consistency. Then each approximated patch is refined by a sparse representation of candidate patches based on local steering kernel (LSK) feature to retain texture quality. We also propose a multiple pyramids based approach to generate several inpainted versions of the input image, one for each of the pyramids. Finally, we combine the inpainted images by gradient-based weighted average to produce the final inpainted image. This approach helps to maintain structure coherence and to remove artifacts which may appear in the inpainted images due to different initial scales of the individual pyramids. The proposed method is tested on a wide range of natural images for scratch and blob/object removal. We have presented both quantitative and qualitative comparison with the existing methods to demonstrate the superiority of the proposed method.

This paper presents a Markov random field (MRF)-based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement. In patch selection, a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only. This improves patch searching in terms of both quality and time. We also propose an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate patches. This eliminates random variation and unwanted artifacts as well. Finally, a weight term is computed, based on the refined patches and is incorporated in the objective function of the MRF model to improve the optimal patch assignment. Experimental results on a large number of natural images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further we extract local steering kernel (LSK) based feature to preserve geometric structure and texture sharpness in the target region. The advantage of non local self similarity as redundancy of similar patches in natural images is introduced to find the candidate patches from the whole source region. Based on these local and non local prior information we propose a sparse representation framework for image inpainting. Our proposed method is tested on wide range of natural images. The experimental results show the superiority of the proposed method compared to some of the previous approaches.

In this paper, we present an exemplar-based image in painting technique using the higher order singular value decomposition (HOSVD). The two main steps of the proposed method are determination of patch priority and patch completion. Here we adopt gradient-based priority term. For patch completion, we build a stack of the candidate patches corresponding to the target patch. Then we find the coefficients matrix of singular values using HOSVD transformation from the stack and nullify some singular values which corresponds to some artifacts. Next, we invert the HOSVD transform and synthesize the target patch by taking weighted average of filtered candidate patches. We also incorporate local patch consistency in the proposed model. Experimental results show the superiority of the proposed method compared to the competitive methods. The proposed method may be used for restoration of digital images of defective or damaged artifacts.