A Review on Image Dehazing Algorithms for Vision based Applications in Outdoor Environment

Vision-based applications deal with the degraded quality of images due to bad environment. During the bad environment, particles such as haze, fog, and mist, etc. diminish the clarity of the scene. Therefore, the information can be lost from such images. The haze removal algorithms play a vital role to improve the quality as well as remove the haze from the image. The objective of this paper is to present a comprehensive study and implementation of various existing dehazing algorithms and their evaluation using realistic single-image dehazing dataset exploited in many vision-based applications. Further, the quantitative and qualitative comparisons of the bench-marked dehazing algorithms are also presented in this paper. For evaluation, various performance measuring criteria including subjective comparison, quantitative comparison, full reference metrics such as peak signal-to-noise ratio and the structural similarity index, no-reference metrics such as spatial-spectral entropy-based quality, and blind image integrity notator using discrete cosine transform statistic has been used. The experimental results highlight the divergence in the various performance metrics used. Furthermore, the comparison among various existing image dehazing and their limitation is highlighted and suggested for future work in this direction.