Super-resolution image reconstruction based on three-step-training neural networks

Zhu, Fengsen; Jin-zong, LI; Zhu, Bing; Ma, Di

doi:10.3969/j.issn.1004-4132.2010.06.002

Cited by 5 publications

(1 citation statement)

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“…Image super-resolution provides a low cost, software-based technique to improve the spatial resolution of an image beyond the limitations of the imaging hardware devices. The areas of application include medical imaging [1] and satellite imaging [2] and high-definition television (HDTV). In such cases, it is standard to assume that the observed low-resolution image(s) is a blurred and downsampled version of the high-resolution image.…”

Section: Introductionmentioning

confidence: 99%

Image Super-Resolution Based on Sparsity Prior via Smoothed $l_0$ Norm

Rostami¹,

Wang²

2016

Preprint

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In this paper we aim to tackle the problem of reconstructing a high-resolution image from a single low-resolution input image, known as single image super-resolution. In the literature, sparse representation has been used to address this problem, where it is assumed that both low-resolution and high-resolution images share the same sparse representation over a pair of coupled jointly trained dictionaries. This assumption enables us to use the compressed sensing theory to find the jointly sparse representation via the low-resolution image and then use it to recover the high-resolution image. However, sparse representation of a signal over a known dictionary is an ill-posed, combinatorial optimization problem. Here we propose an algorithm that adopts the smoothed l0-norm (SL0) approach to find the jointly sparse representation. Improved quality of the reconstructed image is obtained for most images in terms of both peak signal-to-noise-ratio (PSNR) and structural similarity (SSIM) measures.

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Section: Introductionmentioning

confidence: 99%