International Journal of Frontiers in Medicine, 2022, 4(4); doi: 10.25236/IJFM.2022.040407.
Songning Lai1, Xuan Ren2
1School of Information Science and Engineering, Shangdong University, No.72 Binhai Road, Jimo District, Qingdao, Shandong, 266000, China
2School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu, Shanghai, 200093, China
The presence of haze greatly limits the visibility of images and the acquisition of scene details, so it is particularly important to explore how to efficiently dehazed images. Based on this problem, we delve into the nature of haze imaging, model the blurred image and estimate the airlight and transmission coefficients in it, invert the dehazed image and perform image enhancement processing. In the algorithm we introduce methods to speed up the operation of the algorithm such as KD tree clustering algorithm and interpolation algorithm to reduce the complexity of the algorithm. Finally, we validate our algorithm with the official dataset REISDE and obtain very satisfactory results with an overall complexity of O(n). Therefore, our algorithm can process dehazed images very efficiently and quickly.
Dehaze; HE; CLAHE; Airlight; Transmission Coefficient
Songning Lai, Xuan Ren. Image Dehazing and Enhancement Based on Fuzzy Image Modeling. International Journal of Frontiers in Medicine (2022), Vol. 4, Issue 4: 34-41. https://doi.org/10.25236/IJFM.2022.040407.
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