Improving Haze Detection Using Deep Learning-Based Optimal Contrast Limited Adaptive Histogram Equalization

Abstract

When gathering optical satellite pictures, light reflected from the surface due to water vapor, snow, fog, haze, and more tiny particles in the environment is generally seen as a gap in the propagation process. Haze has a greater number of suspended particles like aerosols and water droplets. These particles have absorption effects and scattering in the light. Although haze translucency grants a chance for image restoration, a well-organized and broadly relevant haze removal procedure for holding several hazes is quite an extensive provocation. To address this issue, this paper proposed an Optimal Contrast Limited Adaptive Histogram Equalization (OCLAHE) to capture further intricate features and patterns connected to haze, enabling more accurate haze detection and removal. The deeper network can learn complicated descriptions and recognize between hazy and nonhazy regions with higher precision. The proposed method is validated in I-Haze and O-Haze datasets, and its performance is quantified by various performance metrics such as MSE, SSIM, PSNR, WPSNR, and Running time. The experimental consequences demonstrate that the developed model performs better than other techniques and attains an MSE from both datasets as 0.0054 and 0.0051. Overall, the proposed model amends the accuracy and reliability of haze detection in images.