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- W4285814550 abstract "To solve the problem of texture details loss in super-resolution reconstructed images, a novel image super-resolution algorithm (SCGAN) based on strong constraints with better fidelity was proposed. Firstly, in order to reconstruct finer texture details, an enhanced loss function was designed based on L1 function. Secondly, channel attention mechanism is introduced in super-resolution generative adversarial network architecture, and input feature weight parameters are given. Finally, the algorithm is validated on several benchmark data sets. The experimental results show that SCGAN not only has better visual effect, but also improves the objective evaluation index. Perceived similarity (LPIPS) improved at least 2.3% on average across the five test sets. Excluding RRDB algorithm with poor LPIPS, PSNR and SSIM improved at least 1. 2303dB on average and 0.0408 on average." @default.
- W4285814550 created "2022-07-19" @default.
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- W4285814550 date "2022-05-20" @default.
- W4285814550 modified "2023-09-26" @default.
- W4285814550 title "Image Super Resolution Reconstruction Algorithm Based on Strong Constraint" @default.
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- W4285814550 doi "https://doi.org/10.1109/cvidliccea56201.2022.9824123" @default.
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