Abstract / truncated to 115 words (read the full abstract)

This work is concerned with two image-processing problems, image deconvolution with incomplete observations and data fusion of spectral images, and with some of the algorithms that are used to solve these and related problems. In image-deconvolution problems, the diagonalization of the blurring operator by means of the discrete Fourier transform usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods, or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. We propose a new deconvolution framework for images with incomplete observations that allows one to work with diagonalizable convolution operators, ... toggle 14 keywords

deconvolution incomplete observations hyperspectral imaging superresolution data fusion pansharpening convex nonsmooth optimization primal-dual optimization alternating-direction method of multipliers (admm) semismooth newton method forward-backward method monotone inclusion averaged operator variable metric.


Simões, Miguel
Universidade de Lisboa, Instituto Superior Técnico & Université Grenoble Alpes
Publication Year
Upload Date
Sept. 30, 2021

First few pages / click to enlarge

The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.

The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.