Single image dehazing via combining the prior knowledge and CNNs

TL;DR

This paper introduces an end-to-end single image dehazing method that combines prior knowledge with deep learning, effectively reducing artifacts and noise while improving haze removal quality.

Contribution

It proposes a novel approach integrating prior knowledge and CNNs, including a layered decomposition and adaptive strategy for better dehazing results.

Findings

Achieves superior dehazing performance compared to existing methods.

Effectively reduces noise and halo artifacts in haze removal.

Demonstrates robustness across various hazy scenes.

Abstract

Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method. The haze image is decomposed into the base layer and detail layers through a weighted guided image filter (WGIF) firstly, and the airlight is estimated from the base layer. Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map. To restore object close to the camera completely without amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed based on the value of the transmission map. If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally. Otherwise, the haze image is used. Experiments show that the proposed method achieves superior performance over existing methods.