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image-restoration-and-enhancements
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(ASNA) an attention-based Siamese-difference neural network with surrogate ranking loss function for perceptual image quality assessment
, Article 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, 19 June 2021 through 25 June 2021 ; 2021 , Pages 388-397 ; 21607508 (ISSN); 9781665448994 (ISBN) ; Royat, A ; Sharif University of Technology
IEEE Computer Society
2021
Abstract
Recently, deep convolutional neural networks (DCNN) that leverage the adversarial training framework for image restoration and enhancement have significantly improved the processed images' sharpness. Surprisingly, although these DCNNs produced crispier images than other methods visually, they may get a lower quality score when popular measures are employed for evaluating them. Therefore it is necessary to develop a quantitative metric to reflect their performances, which is well-aligned with the perceived quality of an image. Famous quantitative metrics such as Peak signal-to-noise ratio (PSNR), The structural similarity index measure (SSIM), and Perceptual Index (PI) are not well-correlated...
NTIRE 2021 challenge on perceptual image quality assessment
, Article 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021, 19 June 2021 through 25 June 2021 ; 2021 , Pages 677-690 ; 21607508 (ISSN); 9781665448994 (ISBN) ; Cai, H ; Dong, C ; Ren, J.S ; Qiao, Y ; Gu, S ; Timofte, R ; Cheon, M ; Yoon, S ; Kang, B. K ; Lee, J ; Zhang, Q ; Guo, H ; Bin, Y ; Hou, Y ; Luo, H ; Guo, J ; Wang, Z ; Wang, H ; Yang, W ; Bai, Q ; Shi, S ; Xia, W ; Cao, M ; Wang, J ; Chen, Y ; Yang, Y ; Li, Y ; Zhang, T ; Feng, L ; Liao, Y ; Li, J ; Thong, W ; Pereira, J. C ; Leonardis, A ; McDonagh, S ; Xu, K ; Yang, L ; Cai, H ; Sun, P ; Ayyoubzadeh, M ; Royat, A ; Fezza, A ; Hammou, D ; Hamidouche, W ; Ahn, S ; Yoon, G ; Tsubota, K ; Akutsu, H ; Aizawa, K ; Sharif University of Technology
IEEE Computer Society
2021
Abstract
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image...