OVERVIEW OF METHODS FOR IMPROVING THE VISUAL QUALITY OF IMAGES AND VIDEOS IN ADVERSE WEATHER CONDITIONS
In the modern world, image processing is used in various fields of human activity. Due to various interference factors, image and video deterioration seriously reduces the accuracy and efficiency of target tracking and recognition. Hence, image restoration is becoming a major problem in the field of...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Russian |
| Published: |
North-Caucasus Federal University
2023-01-01
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| Series: | Современная наука и инновации |
| Subjects: | |
| Online Access: | https://msi.elpub.ru/jour/article/view/1370 |
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| Summary: | In the modern world, image processing is used in various fields of human activity. Due to various interference factors, image and video deterioration seriously reduces the accuracy and efficiency of target tracking and recognition. Hence, image restoration is becoming a major problem in the field of computer vision.Matrials and methods, results and discussions This study provides an overview of methods for improving the visual quality of images and videos when they are distorted by weather phenomena. The methods are classified according to the types and differences of weather phenomena. Examples of their approbation on images and video are given. Conclusions are drawn about each of the types of algorithms.Conclusion The article explored various methods to improve the visual quality of images and videos in adverse weather conditions. Each method was studied in detail, their advantages and disadvantages were considered, which made it possible to come to the following conclusions: 1) methods that use physical models are very efficient, but are computationally complex, in this regard, this method is more appropriate to use for image and video post-processing. 2) Methods based on histograms are simpler, but they are only suitable for static weather conditions (fog, haze, haze). 3) The most promising are methods based on learning. Neural networks allow solving more complex problems, due to the possibility of parallelizing information and further learning. Also, these methods do an excellent job of improving the quality of both images and videos. Learning-based methods are applicable to both static weather conditions (fog, haze, haze) and dynamic ones (snow, rain, hail), which makes them more versatile for solving this problem. |
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| ISSN: | 2307-910X |