期刊
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
卷 6, 期 4, 页码 762-782出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETCI.2022.3173443
关键词
Atmospheric modeling; Image restoration; Scattering; Image color analysis; Meteorology; Degradation; Computer vision; Single image dehazing; contrast enhancement; image restoration; traditional methods; deep learning-based approaches; survey
资金
- Grant Agency of Excellence, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic [UHK-FIMGE-2204-2022]
- Slovak Scientific Grant Agency (VEGA) Grant Agency [1/0588/22]
For the last two decades, image processing techniques have been widely used in computer vision applications. This study describes various traditional and deep learning techniques of image dehazing, aiming to provide an intuitive understanding of these techniques and improve the comprehension of the dehazing process.
For the last two decades, image processing techniques have been used frequently in computer vision applications. The most challenging task in image processing is restoring images that are degraded due to various weather conditions. Mainly, the visibility of outdoor images is corrupted due to adverse atmospheric effects. The visibility of acquired images is reduced in these circumstances. Haze is an atmospheric phenomenon that reduces the clarity of an image. Due to the presence of particles such as dust, dirt, soot, or smoke, there is significant decay in the color and contrast of captured images. Haze present in acquired images causes issues in a variety of computer vision applications. Therefore, enhancing the contrast of a hazy image and restoring the visibility of the scene is essential. Since clear images are required in every application, image dehazing is an important step. Hence, many researchers are working on it. Different methods have been presented in the literature for image dehazing. This study describes various traditional and deep learning techniques of image dehazing from an analytical perspective. The main intention behind this work is to provide an intuitive understanding of the major techniques that have made a relevant contribution to haze removal. In this paper, we have covered different types of contributions toward dehazing based on the traditional method as well as deep learning approaches. With a considerable amount of instinctive simplifications, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.
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