IEEE Trans Image Process - Unified blind method for multi-image super-resolution and single/multi-image blur deconvolution.

Tópicos

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Resumo

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.

Resumo Limpo

paper present first time unifi blind method multiimag superresolut misr sr singleimag blur deconvolut sibd multiimag blur deconvolut mibd lowresolut lr imag degrad linear spaceinvari lsi blur alias addit white gaussian nois awgn propos approach base altern minim new cost function respect unknown highresolut hr imag blur regular term hr imag base upon hubermarkov random field hmrf model type variat integr exploit piecewis smooth natur hr imag blur estim process support edgeemphas smooth oper improv qualiti blur estim enhanc strong soft edg toward step edg filter weak structur paramet updat gradual number salient edg use blur estim increas iter better perform blur estim done filter domain rather pixel domain ie use gradient lr hr imag regular term blur gaussian l norm allow fast nonit optim frequenc domain acceler process time sr reconstruct separ upsampl registr process optim procedur simul result synthet reallif imag novel comput imag confirm robust effect propos method

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