Assessing the prospects for robust sub-diffraction limited super-resolution imaging with deep neural networks

2018 ◽  
Author(s):  
Robert P. Nieuwenhuizen ◽  
Dennis D. Bouwman ◽  
Klamer Schutte
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Super Resolution ◽  
Resolution Imaging

scholarly journals Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study

2018 ◽  
Vol 28 (4) ◽  
pp. 735-744 ◽  
Author(s):  
Michał Koziarski ◽  
Bogusław Cyganek
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Dynamic Range ◽  
Super Resolution ◽  
Image Resolution ◽  
Quality Data ◽  
Low Resolution ◽  
The Impact

Abstract Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

Efficient Super-Resolution Method for Single Image based on Deep Neural Networks

2018 ◽  
Vol 55 (6) ◽  
pp. 89-96
Author(s):  
Woojin Jeong ◽  
Hyeon Seok Yang ◽  
Bok Gyu Han ◽  
Jae Jun Sim ◽  
Sejin Park ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Super Resolution ◽  
Single Image ◽  
Resolution Method

Evaluation and development of deep neural networks for image super-resolution in optical microscopy

2021 ◽  
Vol 18 (2) ◽  
pp. 194-202 ◽  
Author(s):  
Chang Qiao ◽  
Di Li ◽  
Yuting Guo ◽  
Chong Liu ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Microscopy ◽  
Deep Neural Networks ◽  
Super Resolution ◽  
Image Super Resolution
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