A multi-exposure fusion framework for contrast enhancement of hazy images employing dynamic stochastic resonance

Current imaging devices coupled with advanced hardware and software are smart enough to enhance low light images taken in clear weather. But in hazy or foggy environments, the captured images are of degraded quality. To address this issue, image processing algorithms are employed to enhance the degraded images to make useful for extracting meaningful features. In this study, we propose a haze removal algorithm to improve the color and contrast of images captured in hazy environments. The first step involves generation of images with various exposures using the theory of dynamic stochastic resonance. The images are then fused in a multi-scale fusion framework crafting weight maps viz. haze density, chromaticity, and luminance gradient. The fusion process focuses on uniformly enhancing the dark and bright regions of the image. However, it may overemphasize haze affected regions. Therefore, in the second step, the atmospheric scattering equation is referred and its modified version is applied that accomplishes the haze removal task. Quantitative and qualitative analyses demonstrate the effectiveness of the proposed method.