Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Recovery of blocky images from noisy and blurred data
SIAM Journal on Applied Mathematics
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Probabilistic image sensor fusion
Proceedings of the 1998 conference on Advances in neural information processing systems II
Remote Sensing, Third Edition: Models and Methods for Image Processing
Remote Sensing, Third Edition: Models and Methods for Image Processing
Guest editorial: Image fusion: Advances in the state of the art
Information Fusion
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
A total variation-based algorithm for pixel-level image fusion
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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In this paper, we propose a Bayesian approach towards fusion of hyperspectral images for the purpose of efficient visualization. Fusion has been posed as an estimation problem where the observed hyperspectral bands have been related to the fused image through a first order model of image formation. The parameters of the model indicate the quality of the pixel captured locally. As visualization is our primary aim of fusion, we expect higher contribution of the ''visually important'' pixels towards the final fused image. We propose a two-step framework for fusion of hyperspectral image, where the first step identifies the quality of each pixel of the data based on some of the local quality measures of the hyperspectral data. Subsequently, we formulate the problem of the estimation of the fused image in a MAP framework. We incorporate the total variation (TV) norm-based prior which preserves the sharp discontinuities in the fused image. The fused images, thus, appear sharp and natural where the edges and boundaries have been retained. We have provided visual as well as quantitative results to substantiate the effectiveness of the proposed technique.