@article {10.3844/jcssp.2024.1805.1817,
article_type = {journal},
title = {Dehazing Mechanism Using Auto-Encoder with Intensity Attention System},
author = {Tiwari, Rajat and Goyal, Bhawna and Dogra, Ayush},
volume = {20},
number = {12},
year = {2024},
month = {Nov},
pages = {1805-1817},
doi = {10.3844/jcssp.2024.1805.1817},
url = {https://thescipub.com/abstract/jcssp.2024.1805.1817},
abstract = {In the modern world, images play a significant medium for communication. Primarily, it is easily transferred and disseminated across various platforms which allows the people to express their ideology and perceptions. Conversely, images can be prone the environmental conditions as the image quality can be affected by the weather circumstances. Particularly, haze images minimize the whole clarity and visibility of the image. It is necessary to dehazed the image to retain the image quality and enhance the clarity of the image. Conventionally, the manual dehazing method includes altering several parameters and utilization of image editing software. It is a time-consuming mechanism, less efficient, and can be prone to manual error. To resolve the issue, traditional researchers utilized various techniques for the dehazing mechanism but lacked accuracy and speed. To address the issue, the proposed research employs an encoder that uses focus flex and entropy fade component blocks with an attention mechanism for the dehazing model. Moreover, the attention mechanism is used to highlight substantial data to enhance accuracy. Correspondingly, dense-haze and FRIDA datasets are used for the dehazing function to augment the efficiency. Accordingly, the respective model is evaluated with the performance metrics to examine its efficiency. Furthermore, comparative analysis is carried out to reveal the presented research's greater performance.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}