scholarly journals Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1907
Author(s):  
Hyung-Il Kim ◽  
Seok Bong Yoo
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Imaging Techniques ◽  
Super Resolution ◽  
Poor Quality ◽  
Image Resolution ◽  
Closed Circuit ◽  
High Definition ◽  
Black Boxes ◽  
Optical Blur

Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.

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

scholarly journals Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

2021 ◽  
Vol 3 (4) ◽  
pp. 966-989
Author(s):  
Vanessa Buhrmester ◽  
David Münch ◽  
Michael Arens
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Black Box ◽  
Complex Data ◽  
Comprehensive Overview ◽  
Nonlinear Structure ◽  
Black Boxes ◽  
Insight Into

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality. By basing decision-making algorithms on Deep Neural Networks, prejudice and unfairness may be promoted unknowingly due to a lack of transparency. Hence, several so-called explanators, or explainers, have been developed. Explainers try to give insight into the inner structure of machine learning black boxes by analyzing the connection between the input and output. In this survey, we present the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about the taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

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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