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All Journal International Journal of Electrical and Computer Engineering
Suresh Kaggere Veeranna
Siddaganga Institute of Technology
Computer Science & IT Electrical & Electronics Engineering

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Super resolution image reconstruction via dual dictionary learning in sparse environment Shashi Kiran Seetharamaswamy; Suresh Kaggere Veeranna
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4970-4977

Abstract

Patch-based super resolution is a method in which spatial features from a low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted. These coefficients obtained are used to recover HR patch. One dictionary is trained for LR image patches, and another dictionary is trained for HR image patches and both dictionaries are jointly trained. In the proposed method, high frequency (HF) details required are treated as combination of main high frequency (MHF) and residual high frequency (RHF). Hence, dual-dictionary learning is proposed for main dictionary learning and residual dictionary learning. This is required to recover MHF and RHF respectively for recovering finer image details. Experiments are carried out to test the proposed technique on different test images. The results illustrate the efficacy of the proposed algorithm.
Co-Authors Shashi Kiran Seetharamaswamy