A novel regularized edge-preserving super-resolution algorithm

2013 ◽  
Author(s):  
Hui Yu ◽  
Fu-sheng Chen ◽  
Zhi-jie Zhang ◽  
Chen-sheng Wang
Keyword(s):  
Super Resolution ◽  
Edge Preserving ◽  
Resolution Algorithm

scholarly journals A Novel Fast 3d Single Image Super-Resolution Algorithm

2021 ◽  
Author(s):  
Nwigbo Kenule Tuador ◽  
Duong Hung Pham ◽  
Jerome Michetti ◽  
Adrian Basarab ◽  
Denis Kouame
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution

A Video Super Resolution Algorithm Based on Wavelet Coefficient

2014 ◽  
Vol 610 ◽  
pp. 425-428
Author(s):  
Wei Jian Liu ◽  
Si Da Xiao ◽  
Ruo He Yao
Keyword(s):  
High Frequency ◽  
Wavelet Coefficient ◽  
Three Dimensional ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Sequences ◽  
Discrete Wavelet ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Visual Results

In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

scholarly journals Region Filling with Super Resolution Algorithm

2021 ◽  
Vol 9 (VI) ◽  
pp. 3557-3564
Author(s):  
Darakhshan R. Khan
Keyword(s):  
Patch Size ◽  
Research Work ◽  
Super Resolution ◽  
Input Image ◽  
Computational Time ◽  
Low Resolution ◽  
Resolution Image ◽  
Resolution Algorithm ◽  
Region Filling

Region filling which has another name inpainting, is an approach to find the values of missing pixels from data available in the remaining portion of the image. The missing information must be recalculated in a distinctly convincing manner, such that, image look seamless. This research work has built a methodology for completely automating patch priority based region filling process. To reduce the computational time, low resolution image is constructed from input image. Based on texel of an image, patch size is determined. Several low resolution image with missing region filled is generated using region filling algorithm. Pixel information from these low resolution images is consolidated to produce single low resolution region filled image. Finally, super resolution algorithm is applied to enhance the quality of image and regain all specifics of image. This methodology of identifying patch size based on input fed has an advantage over filling algorithms which in true sense automate the process of region filling, to deal with sensitivity in region filling, algorithm different parameter settings are used and functioning with coarse version of image will notably reduce the computational time.

Super-resolution algorithm based on weighted vector quantization

2014 ◽  
Vol 23 (2) ◽  
pp. 023002
Author(s):  
Tse-Ming Kuo ◽  
Shen-Chuan Tai
Keyword(s):  
Vector Quantization ◽  
Super Resolution ◽  
Resolution Algorithm

scholarly journals Multi-frame super-resolution algorithm for complex motion patterns

2013 ◽  
Vol 21 (17) ◽  
pp. 19850 ◽  
Author(s):  
A. V. Kanaev ◽  
C. W. Miller
Keyword(s):  
Super Resolution ◽  
Complex Motion ◽  
Motion Patterns ◽  
Resolution Algorithm
Sign in / Sign up

Export Citation Format

Share Document