Single Image Dehazing Algorithm Based on Sky Region Segmentation

TL;DR

This paper introduces a hybrid image dehazing method that combines region segmentation, an improved dark channel prior, and deep learning to effectively dehaze sky and non-sky regions, enhancing image quality and processing speed.

Contribution

It presents a novel hybrid approach that segments sky regions and applies tailored dehazing algorithms, improving over traditional methods in color preservation and computational efficiency.

Findings

Addresses sky region color distortion in foggy images

Improves image quality metrics like entropy and visibility

Achieves fast computation times

Abstract

In this paper a hybrid image defogging approach based on region segmentation is proposed to address the dark channel priori algorithm's shortcomings in de-fogging the sky regions. The preliminary stage of the proposed approach focuses on the segmentation of sky and non-sky regions in a foggy image taking the advantageous of Meanshift and edge detection with embedded confidence. In the second stage, an improved dark channel priori algorithm is employed to defog the non-sky region. Ultimately, the sky area is processed by DehazeNet algorithm, which relies on deep learning Convolutional Neural Networks. The simulation results show that the proposed hybrid approach in this research addresses the problem of color distortion associated with sky regions in foggy images. The approach greatly improves the image quality indices including entropy information, visibility ratio of the edges, average gradient, and the saturation percentage with a very fast computation time, which is a good indication of the excellent performance of this model.