WLDISR: Weighted Local Sparse Representation-Based Depth Image Super-Resolution for 3D Video System

2019 ◽  
Vol 28 (2) ◽  
pp. 561-576 ◽  
Author(s):  
Huan Zhang ◽  
Yun Zhang ◽  
Hanli Wang ◽  
Yo-Sung Ho ◽  
Shengzhong Feng
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
3D Video ◽  
Depth Image ◽  
Video System ◽  
Image Super Resolution ◽  
3D Video System ◽  
Local Sparse Representation

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

Image super‐resolution via adaptive sparse representation and self‐learning

2018 ◽  
Vol 12 (5) ◽  
pp. 753-761 ◽  
Author(s):  
Jianwei Zhao ◽  
Tiantian Sun ◽  
Feilong Cao
Keyword(s):  
Sparse Representation ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Self Learning

Edge area constraint guided filter depth image super-resolution reconstruction algorithm

2021 ◽  
Vol 50 (1) ◽  
pp. 20200081-20200081
Author(s):  
武军安 Jun''an Wu ◽  
郭锐 Rui Guo ◽  
刘荣忠 Rongzhong Liu ◽  
柯尊贵 Zungui Ke ◽  
赵旭 Xu Zhao
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Guided Filter ◽  
Depth Image ◽  
Image Super Resolution
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