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We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the… Expand Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal… Expand Bregman methods introduced in [S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, Multiscale Model. Simul., 4 (2005), pp. 460… Expand The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as… Expand This paper presents an overview of various deconvolution techniques of 3D fluorescence microscopy images. It describes the… Expand This article reviews different deconvolution methods. The all-pervasive presence of noise is what makes deconvolution… Expand We describe and apply an iterative, time-domain deconvolution approach to receiver-function estimation and illustrate the… Expand Convolution and related concepts, P.A. Jansson distortion of optical spectra, P.A. Jansson traditional linear deconvolution… Expand The goal of image restoration is to reconstruct the original scene from a degraded observation. This recovery process is critical… Expand Magnetic‐survey data in grid form may be interpreted rapidly for source positions and depths by deconvolution using Euler’s… Expand