Single image resolution enhancement by efficient dilated densely connected residual network

2019 ◽  
Vol 79 ◽  
pp. 13-23 ◽  
Author(s):  
Pourya Shamsolmoali ◽  
Xiaofang Li ◽  
Ruili Wang
Keyword(s):  
Resolution Enhancement ◽  
Image Resolution ◽  
Single Image ◽  
Residual Network

Kernel-attended residual network for single image super-resolution

2021 ◽  
Vol 213 ◽  
pp. 106663
Author(s):  
Yujie Dun ◽  
Zongyang Da ◽  
Shuai Yang ◽  
Yao Xue ◽  
Xueming Qian
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

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.

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