Authors:
Saulo R. S. Reis
1
and
Graça Bressan
2
Affiliations:
1
Federal University of Mato Grosso and UFMT, Brazil
;
2
Polytechnic School of the University of São Paulo-EPUSP, Brazil
Keyword(s):
Super-resolution, Sparse Representation, DCT Interpolation, k-SVD, OMP.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Registration
;
Visual Attention and Image Saliency
Abstract:
In a scenario where acquisition systems have limited resources or available images do not have good
quality, super-resolution (SR) techniques are an excellent alternative for improving the image quality. The
traditional SR methods proposed in the literature are effective in HR image reconstruction to a
magnification factor up to 2. In recent years, example-based SR methods have shown excellent results in the
HR image reconstruction to magnification factor 3 or more. In this paper, we propose a scalable and
iterative algorithm for single-image SR using a two-step strategy with DCT interpolation and the sparse-based
learning method. The method proposed implements some improvements in the dictionary training and
the reconstruction process. A new dictionary is built by using an unsharp mask technique for feature
extraction. The idea is to reduce the learning time by using two different small dictionaries. The results were
compared with others interpolation-based and SR methods and demons
trated the effectiveness of the
algorithm proposed in terms of PSNR, SSIM and Visual Quality.
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