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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward byExpand
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Weighted Nuclear Norm Minimization with Application to Image Denoising
As a convex relaxation of the low rank matrix factorization problem, the nuclear norm minimization has been attracting significant research interest in recent years. The standard nuclear normExpand
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Learning Deep CNN Denoiser Prior for Image Restoration
Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds ofExpand
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FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level,Expand
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Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision
As a convex relaxation of the rank minimization model, the nuclear norm minimization (NNM) problem has been attracting significant research interest in recent years. The standard NNM regularizes eachExpand
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Projective dictionary pair learning for pattern classification
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the inputExpand
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Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume thatExpand
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NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2KExpand
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Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue byExpand
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Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification
Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g.,Expand
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