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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising, and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance. Expand
Weighted Nuclear Norm Minimization with Application to Image Denoising
Experimental results clearly show that the proposed WNNM algorithm outperforms many state-of-the-art denoising algorithms such as BM3D in terms of both quantitative measure and visual perception quality. Expand
Learning Deep CNN Denoiser Prior for Image Restoration
Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications. Expand
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance, and enjoys several desirable properties, including the ability to handle a wide range of noise levels effectively with a single network. Expand
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many areExpand
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision
It is proved that WNNP is equivalent to a standard quadratic programming problem with linear constrains, which facilitates solving the original problem with off-the-shelf convex optimization solvers and presents an automatic weight setting method, which greatly facilitates the practical implementation of WNNM. Expand
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Extensive experimental results show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications. Expand
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
The spatial-temporal regularized correlation filters (STRCF) formulation can not only serve as a reasonable approximation to SRDCF with multiple training samples, but also provide a more robust appearance model thanSRDCF in the case of large appearance variations. Expand
AttGAN: Facial Attribute Editing by Only Changing What You Want
The proposed method is extended for attribute style manipulation in an unsupervised manner and outperforms the state-of-the-art on realistic attribute editing with other facial details well preserved. Expand
Projective dictionary pair learning for pattern classification
Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks. Expand