2016
DOI: 10.1186/s13640-016-0125-6
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Super-resolution via a fast deconvolution with kernel estimation

Abstract: Image super-resolution has wide applications in biomedical imaging, computer vision, image recognition, etc. In this paper, we present a fast single-image super-resolution method based on deconvolution strategy. The deconvolution process is implemented via a fast total variation deconvolution (FTVd) method that runs very fast. In particular, due to the inaccuracy of kernel, we utilize an iterative strategy to correct the kernel. The experimental results show that the proposed method can improve image resolutio… Show more

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Cited by 4 publications
(2 citation statements)
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References 42 publications
(71 reference statements)
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“…Then, the blur kernel is estimated by modelling statistical irregularities in the power spectrum of blurred natural images [10]. Then, the deconvolution process is performed adjusting the blur kernel employing a fast total variation deconvolution FTVd (Fast Total Variation deconvolution) method [20]. After deconvolution process, a sharpening filter is used to mitigate the smoothest, to enhance the borders and to obtain an image free of impulsive noise.…”
Section: Case Studymentioning
confidence: 99%
“…Then, the blur kernel is estimated by modelling statistical irregularities in the power spectrum of blurred natural images [10]. Then, the deconvolution process is performed adjusting the blur kernel employing a fast total variation deconvolution FTVd (Fast Total Variation deconvolution) method [20]. After deconvolution process, a sharpening filter is used to mitigate the smoothest, to enhance the borders and to obtain an image free of impulsive noise.…”
Section: Case Studymentioning
confidence: 99%
“…Ways for resolution improvement can be broadly classified into two families of methods: obtaining data with higher discretization [7,8] and reducing the influence of the instrumental function which is wider than sample size [5,6,[9][10][11].…”
Section: Introductionmentioning
confidence: 99%