Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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Advances in Japanese Business and Economicstion information for image reconstruction. Such sequential approaches suffer from two fundamental weaknesses - i.e., the lack of robustness (the performance drops when the estimated degradation is inaccurate) and the lack of transparency (network architectures are heuristic without incorporating dom

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Computer Vision – ECCV 2022978-3-031-19797-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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,Dynamic Dual Trainable Bounds for?Ultra-low Precision Super-Resolution Networks,d lower bounds to tackle the highly asymmetric activations. 2) A dynamic gate controller to adaptively adjust the upper and lower bounds at runtime to overcome the drastically varying activation ranges over different samples. To reduce the extra overhead, the dynamic gate controller is quantized to

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,VQFR: Blind Face Restoration with?Vector-Quantized Dictionary and?Parallel Decoder,ity. 2). To further fuse low-level features from inputs while not “contaminating” the realistic details generated from the VQ codebook, we proposed a parallel decoder consisting of a texture decoder and a main decoder. Those two decoders then interact with a texture warping module with deformable co

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,Uncertainty Learning in?Kernel Estimation for?Multi-stage Blind Image Super-Resolution, prior and the estimated kernel. We have also developed a novel approach of estimating both the scale prior coefficient and the local means of the LSM model through a deep convolutional neural network (DCNN). All parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimize