Abstract
This paper proposes dark channel prior algorithm to remove smog from an image. Smog degrades the quality of outdoor pictures, and image quality has been lost considerably. An important problem is that of the elimination of hazards, which is called dehazing. The turbid material in the atmosphere (e.g., contaminants and droplets), typically degrades images of natural scenes. The decayed pictures lack color fidelity and contrast. The deterioration is spatially dependent, because the volume of dispersion depends on the scene point distance from the camera. In this work, a simple but effective picture is presented before the dark channel which is stripped. The previous dark channel is a kind of outdoor camera data. It is based on a crucial observation. A GUI is created using MATLAB in this paper. Using this GUI, image is encrypted first and then compressed (EtC) using which the thickness of the smog can be measured directly and a high-quality smog-free image can be obtained. The effectiveness of the proposed method is validated through the results obtained by dehazing the images.
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Saini, A., Sharma, A., Rautela, K. (2021). Single Dark Channel Prior Generalization of Smoggy Image. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_51
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DOI: https://doi.org/10.1007/978-981-15-9689-6_51
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