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Our algorithm first transforms the input low resolution (LR)-IR image into its low-frequency and high-frequency subbands using the discrete wavelet decomposition. Subsequently, we introduce the optimized PST to operate on the LR-IR image and extract the intrinsic edge structure. The PST behaves differently at low-frequency and high-frequency regions, thus capturing the intensity variations for edge detection. We incorporate the PST extracted edge map in the wavelet subbands to preserve the intrinsic structure of images. The resultant subbands are further refined based on the missing residuals obtained using the WRESNET. The proposed method is validated through quantitative and qualitative evaluations against the conventional and state-of-art SR methods. Results reveal that the proposed method outperforms the existing methods. <\/jats:p>","DOI":"10.1142\/s021946782150008x","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T15:05:00Z","timestamp":1598367900000},"page":"2150008","source":"Crossref","is-referenced-by-count":4,"title":["Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform"],"prefix":"10.1142","volume":"21","author":[{"given":"Gunnam","family":"Suryanarayana","sequence":"first","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Department of Electronics and Communication, V. R. S. E. C, Vijayawada, Andhra Pradesh, India"}]},{"given":"Kandala N. V. P. 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