Infrared and Laser Engineering, Volume. 51, Issue 3, 20210468(2022)
SR reconstruction algorithm of regularization based on improve of sparse representation
Figures & Tables(10)
Fig. 1. Sparse representation model in the transform domain
Fig. 4. Schematic diagram of quadratic function is used to gradually approximate the optimized problem
Fig. 6. Results of each SR reconstruction algorithm in the simulation experiment
Fig. 8. The results of each SR reconstruction algorithm in the actual test experiment
Table 1. PSNR results of each SR reconstruction algorithm (Unit: dB)
View tableView in ArticleTable 1. PSNR results of each SR reconstruction algorithm (Unit: dB)
Test images Tower Boat House Aircraft Average Sparse represent-ation regulariza-tion 34.59 22.45 27.23 30.21 28.62 Autoregressive regulariza-tion 34.65 22.53 27.51 30.32 28.74 Non-local similarity regulariza-tion 34.79 22.36 27.60 30.67 28.85 Proposed algorithm 34.92 22.71 28.69 31.33 29.41
Table 2. SNR and IE comparison results of each SR reconstruction algorithm
View tableView in ArticleTable 2. SNR and IE comparison results of each SR reconstruction algorithm
Evaluation index Sparse representation regularization Autoregressive regularization Non-local similarity regularization The proposed algorithm SNR 2.12 3.71 4.68 6.12 IE 2.31 2.86 3.23 4.23
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Bing Xie, Shuhui Wan, Yunhua Yin. SR reconstruction algorithm of regularization based on improve of sparse representation[J]. Infrared and Laser Engineering, 2022, 51(3): 20210468
Paper Information
Category: Image processing
Received: Dec. 12, 2021
Accepted: --
Published Online: Apr. 8, 2022
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