Single depth map super-resolution via joint non-local self-similarity modeling and local multi-directional gradient guided regularization

2021 ◽  
pp. 116313
Author(s):  
Yingying Zhang ◽  
Chao Ren ◽  
Honggang Chen ◽  
Ce Zhu ◽  
Kai Liu
Keyword(s):  
Super Resolution ◽  
Depth Map ◽  
Self Similarity ◽  
Non Local

scholarly journals Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

2021 ◽  
Vol 15 (1) ◽  
pp. 170-179
Author(s):  
Kathiravan Srinivasan ◽  
Ramaneswaran Selvakumar ◽  
Sivakumar Rajagopal ◽  
Dimiter Georgiev Velev ◽  
Branislav Vuksanovic
Keyword(s):  
Magnetic Resonance ◽  
Super Resolution ◽  
Mr Images ◽  
Self Similarity ◽  
Significant Research ◽  
Non Local ◽  
Brain Mr Images ◽  
Texture Detection ◽  
Sr Method ◽  
Robust To Noise

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

A New Super-Resolution Reconstruction Algorithm Based on Block Sparse Representation

2013 ◽  
Vol 710 ◽  
pp. 603-607
Author(s):  
Xiao Dong Zhao ◽  
Jian Zhong Cao ◽  
Hui Zhang ◽  
Guang Sen Liu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Training Data ◽  
Self Similarity ◽  
Image Block ◽  
Non Local ◽  
Simulation Results ◽  
Sr Reconstruction ◽  
Suppress Noise

In this paper, we propose a new single super-resolution (SR) reconstruction algorithm via block sparse representation and regularization constraint. Firstly, discrete K-L transform is used to learn compression sub-dictionary according to the specific image block. Combined with threshold choice of training data, the transform bases are generated adaptively corresponding to the sparse domain. Secondly, Non-local Self-similarity (NLSS) regularization term is introduced into sparse reconstruction objective function as a prior knowledge to optimize reconstruction result. Simulation results validate that the proposed algorithm achieves much better results in PSNR and SSIM. It can both enhance edge and suppress noise effectively, which proves better robustness.

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