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- W4361185701 abstract "Due to the large amount of information loss in low-resolution images, it is a challenging task to further develop super-resolution reconstruction techniques for single images. Reference-based super-resolution techniques are considered feasible in recovering high-frequency details of images, etc., but most existing algorithms do not consider the color distribution and illumination distribution of the input image and its reference image. Different, so as not to get very good visual needs. In response to the above problems, we propose a super-resolution model based on texture transfer combined with residual network. By introducing perceptual loss, adversarial loss, reconstruction loss and texture loss to form a new loss function, adjusting the weights of different loss items to optimize the loss function, the ability to reconstruct image details is improved. Extensive experiments show that our proposed model has higher PSNR and SSIM than other methods on the CUFED5, Sun80 and Urban100 test sets." @default.
- W4361185701 created "2023-03-31" @default.
- W4361185701 creator A5011407811 @default.
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- W4361185701 date "2023-03-29" @default.
- W4361185701 modified "2023-10-18" @default.
- W4361185701 title "Reference-based super-resolution with texture transformer and residual network" @default.
- W4361185701 doi "https://doi.org/10.1117/12.2668143" @default.
- W4361185701 hasPublicationYear "2023" @default.
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