scholarly journals Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xuan Zhu ◽  
Xianxian Wang ◽  
Jun Wang ◽  
Peng Jin ◽  
Li Liu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Training Image ◽  
Image Features ◽  
Single Image ◽  
Edge Structure ◽  
Image Patches ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Edge Features

Sparse representation has recently attracted enormous interests in the field of image super-resolution. The sparsity-based methods usually train a pair of global dictionaries. However, only a pair of global dictionaries cannot best sparsely represent different kinds of image patches, as it neglects two most important image features: edge and direction. In this paper, we propose to train two novel pairs of Direction and Edge dictionaries for super-resolution. For single-image super-resolution, the training image patches are, respectively, divided into two clusters by two new templates representing direction and edge features. For each cluster, a pair of Direction and Edge dictionaries is learned. Sparse coding is combined with the Direction and Edge dictionaries to realize super-resolution. The above single-image super-resolution can restore the faithful high-frequency details, and the POCS is convenient for incorporating any kind of constraints or priors. Therefore, we combine the two methods to realize multiframe super-resolution. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed method. Experimental results demonstrate that our method can recover better edge structure and details.

scholarly journals Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

2020 ◽  
Vol 10 (1) ◽  
pp. 375 ◽  
Author(s):  
Zetao Jiang ◽  
Yongsong Huang ◽  
Lirui Hu
Keyword(s):  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Features ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Adversarial Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To further enhance the visual quality, we propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN). A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the total amount of parameters. For the discriminator network, the batch normalization (BN) layer was discarded, and the problem of artifacts was reduced. A frequency energy similarity loss function was designed to constrain the generator network to generate better super-resolution images. Experiments on several different datasets showed that the peak signal-to-noise ratio (PSNR) was improved by more than 3 dB, structural similarity index (SSIM) was increased by 16%, and the total parameter was reduced to 42.8% compared with the original model. Combining various objective indicators and subjective visual evaluation, the algorithm was shown to generate richer image details, clearer texture, and lower complexity.

Single image super-resolution by clustered sparse representation and adaptive patch aggregation

2013 ◽  
Vol 10 (5) ◽  
pp. 50-61 ◽  
Author(s):  
Huang Wei ◽  
Xiao Liang ◽  
Wei Zhihui ◽  
Fei Xuan ◽  
Wang Kai
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Single Image Super-Resolution Based on Sparse Representation with Adaptive Dictionary Selection

2016 ◽  
Vol 30 (07) ◽  
pp. 1654006 ◽  
Author(s):  
Xin Li ◽  
Jie Chen ◽  
Ziguan Cui ◽  
Minghu Wu ◽  
Xiuchang Zhu
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Computation Time ◽  
Resolution Image ◽  
Image Patches ◽  
Different Types ◽  
Image Super Resolution ◽  
Sr Reconstruction ◽  
Single Image Super Resolution

Sparse representation theory has attracted much attention, and has been successfully used in image super-resolution (SR) reconstruction. However, it could only provide the local prior of image patches. Field of experts (FoE) is a way to develop the generic and expressive prior of the whole image. The algorithm proposed in this paper uses the FoE model as the global constraint of SR reconstruction problem to pre-process the low-resolution image. Since a single dictionary could not accurately represent different types of image patches, our algorithm classifies the sample patches composed of pre-processed image and high-resolution image, obtains the sub-dictionaries by training, and adaptively selects the most appropriate sub-dictionary for reconstruction according to the pyramid histogram of oriented gradients feature of image patches. Furthermore, in order to reduce the computational complexity, our algorithm makes use of edge detection, and only applies SR reconstruction based on sparse representation to the edge patches of the test image. Nonedge patches are directly replaced by the pre-processing results of FoE model. Experimental results show that our algorithm can effectively guarantee the quality of the reconstructed image, and reduce the computation time to a certain extent.

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