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Joint Dictionary Learning for Example-based Image Super-resolution
In this paper, we propose a new joint dictionary learning method for exa...
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A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods...
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GUN: Gradual Upsampling Network for single image super-resolution
In this paper, we propose an efficient super-resolution (SR) method base...
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Kernel based low-rank sparse model for single image super-resolution
Self-similarity learning has been recognized as a promising method for s...
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Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints
Sparsity constrained single image super-resolution (SR) has been of much...
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Image Super-resolution via Feature-augmented Random Forest
Recent random-forest (RF)-based image super-resolution approaches inheri...
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Shadow Optimization from Structured Deep Edge Detection
Local structures of shadow boundaries as well as complex interactions of...
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Local Patch Classification Based Framework for Single Image Super-Resolution
Recent learning-based super-resolution (SR) methods often focus on the dictionary learning or network training. In this paper, we detailedly discuss a new SR framework based on local classification instead of traditional dictionary learning. The proposed efficient and extendible SR framework is named as local patch classification (LPC) based framework. The LPC framework consists of a learning stage and a reconstructing stage. In the learning stage, image patches are classified into different classes by means of the proposed local patch encoding (LPE), and then a projection matrix is computed for each class by utilizing a simple constraint. In the reconstructing stage, an input LR patch can be simply reconstructed by computing its LPE code and then multiplying corresponding projection matrix. Furthermore, we establish the relationship between the proposed method and the anchored neighborhood regression based methods; and we also analyze the extendibility of the proposed framework. The experimental results on several image sets demonstrate the effectiveness of the proposed framework.
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