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Super-resolution (SR) task has become an important research area due to the rapidly growing interest for high quality images in various computer vision and pattern recognition applications. This has led to the emergence of various SR approaches. According to the number of input images, two kinds of approaches could be distinguished: single or multi-input based approaches. Certainly, processing multiple...
Sparse coding has shown to be an effective technique in solving various reconstruction tasks such as denoising, in painting, and resolution enhancement of natural images. In this paper, we explore the use of this technique specifically to deal with low-resolution and degraded textual images. Firstly, we propose a sparse coding based resolution enhancement approach to recover a textual image with higher...
Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate...
This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from a suitable dictionary. In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single...
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