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.

scholarly journals Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

2021 ◽  
Vol 6 (1) ◽  
pp. 1-6
Author(s):  
Ayush Singh ◽  
Mehran Ebrahimi
Keyword(s):  
Video Sequence ◽  
Structural Information ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Edge Information ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.

scholarly journals Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

2021 ◽  
Vol 13 (9) ◽  
pp. 1854
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Detection Performance ◽  
Image Resolution ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

scholarly journals Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 58791-58801 ◽  
Author(s):  
Yuantao Chen ◽  
Jin Wang ◽  
Xi Chen ◽  
Mingwei Zhu ◽  
Kai Yang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Self Similarity ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution
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