A Review on Deep Learning-based approaches for Image Dehazing

Abstract

Images captured in unpredictable weather conditions frequently suffer from significant degradation. The scattering and absorption of airborne particles in the atmosphere effect on image quality such as poor visibility, low contrast, and color distortions. The problem of image degradation is addressed by many computer vision applications in unpredictable weather conditions as these conditions diminish the clarity of the visual scene due to loss of image details. The learning-based image dehazing approaches play an imperative role to eliminate haze and enhance the quality of haze-free image. This paper presents a review of different learning-based image dehazing approaches which employ different techniques to approximate atmospheric light and transmission map to restore a haze-free image with image details and color fidelity.

Keywords: Image dehazing; Image degradation; Image quality; Weather; Transmission map; Atmospheric light.