Segmentation-based scale-invariant nonlocal means super resolution

2014 ◽  
Author(s):  
Saboya Yang ◽  
Jiaying Liu ◽  
Qiaochu Li ◽  
Zongming Guo
Keyword(s):  
Super Resolution ◽  
Scale Invariant ◽  
Nonlocal Means

Super-resolution reconstruction using nonlocal means

2013 ◽  
Vol 21 (6) ◽  
pp. 1576-1585 ◽  
Author(s):  
李家德 LI Jia-de ◽  
张叶 ZHANG Ye ◽  
贾平 JIA Ping
Keyword(s):  
Super Resolution ◽  
Nonlocal Means

scholarly journals Improved Super-Resolution Ultrasound Microvessel Imaging With Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

2018 ◽  
Vol 65 (2) ◽  
pp. 149-167 ◽  
Author(s):  
Pengfei Song ◽  
Joshua D. Trzasko ◽  
Armando Manduca ◽  
Runqing Huang ◽  
Ramanathan Kadirvel ◽  
...  
Keyword(s):  
Bipartite Graph ◽  
Super Resolution ◽  
Nonlocal Means

Superpixel-guided nonlocal means for image denoising and super-resolution

2016 ◽  
Vol 124 ◽  
pp. 173-183 ◽  
Author(s):  
Xiaoyan Li ◽  
Hongjie He ◽  
Ruxin Wang ◽  
Jun Cheng
Keyword(s):  
Image Denoising ◽  
Super Resolution ◽  
Nonlocal Means

scholarly journals Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

2009 ◽  
Vol 18 (1) ◽  
pp. 36-51 ◽  
Author(s):  
M. Protter ◽  
M. Elad ◽  
H. Takeda ◽  
P. Milanfar
Keyword(s):  
Super Resolution ◽  
Nonlocal Means

scholarly journals Sparse Representation Based Super-Resolution Algorithm using Wavelet Domain Interpolation and Nonlocal Means

2015 ◽  
Vol 16 (2) ◽  
pp. 296
Author(s):  
Gunnam Suryanarayana ◽  
Ravindra Dhuli
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Image Resolution ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Nonlocal Means ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution ◽  
Enhancement Algorithm

In this correspondence, we propose a novel image resolution enhancement algorithm based on discretewavelet transform (DWT), stationary wavelet transform (SWT) and sparse signal recovery of the inputimage. The nonlocal means filter is employed in the preliminary denoising stage of the proposed method.The denoised input low resolution (LR) image is then decomposed into different frequency subbands byemploying DWT and SWT simultaneously. In parallel, the denoised LR image is subjected to a sparse signalrepresentation based interpolation. All the estimated high frequency subbands as well as the sparseinterpolated LR image are fused to generate a high resolution (HR) image by using inverse discrete wavelettransform (IDWT). Experimental results on various test images show the superiority of our method over theconventional and state-of-the-art single image super- resolution (SR) techniques in achieving the real timeperformance.

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