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

ODIUM

落叶剂

,Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction,n (SFI) block in two formats tailored to these two sub-networks, which interactively process the local spatial features and the global frequency information to encourage the complementary learning. Extensive experiments demonstrate that our method achieves superior results than other approaches with

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,Frequency and Spatial Dual Guidance for Image Dehazing,al domain. Extensive experiments on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art approaches both visually and quantitatively. Our code is released publicly at ..

Hallowed

,Learning Discriminative Shrinkage Deep Networks for Image Deconvolution,rties of the Maxout function and develop a deep CNN model with Maxout layers to learn discriminative shrinkage functions, which directly approximates the solutions of these two sub-problems. Moreover, the fast-Fourier-transform-based image restoration usually leads to ringing artifacts. At the same

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,KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution,ear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current repres

hermetic

ARM: Any-Time Super-Resolution Method, computation-performance tradeoff. Moreover, each SISR subnet shares weights of the ARM supernet, thus no extra parameters are introduced. The setting of multiple subnets can well adapt the computational cost of SISR model to the dynamically available hardware resources, allowing the SISR task to be

venous-leak

DUST

,RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos,bi-directional hole filling techniques to alleviate the artifacts of the image synthesis. In the E-step, RIPR renders new images to create a large quantity of training data. In the M-step, we utilize the generated training data to train an optical flow network, which can be used to estimate optical

geometrician

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