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- W4285409146 abstract "The image super-resolution algorithm based on generative adversarial network has the problem of imperfect extraction of detailed texture features, and the network is difficult to converge in the training process. Therefore, a new image super-resolution method: MR-SRGAN (Multi-branch receptive field dense block improved super-resolution generative adversarial network) is proposed. MR-SRGAN extracts image detail texture features through the new MBRS residual block and MRB module, then adjusts the loss function of the model to make the training easier to converge. Finally, in the super-resolution comparison experiment on Set5 and Set14 datasets, under the magnification of 2 and 4 times, the PSNR value of this algorithm is 0.36dB (Set $5 times 2)$, 0.15dB (Set $14 times 2)$, 0.63dB (Set $5 times 4)$, 0.57dB (Set $14 times 4)$ higher than SRGAN algorithm." @default.
- W4285409146 created "2022-07-14" @default.
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- W4285409146 date "2022-05-13" @default.
- W4285409146 modified "2023-09-24" @default.
- W4285409146 title "Image Super-Resolution Method Based on Improved Generative Adversarial Network" @default.
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- W4285409146 doi "https://doi.org/10.1109/icet55676.2022.9824258" @default.
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