Single Image Dehazing through Improved Atmospheric Light Estimation

TL;DR

This paper introduces a novel single image dehazing technique that accurately estimates atmospheric light using a semi-globally adaptive filter, resulting in clearer images with enhanced details and reduced noise, especially useful for outdoor and smart vehicle applications.

Contribution

The proposed method improves atmospheric light estimation by avoiding hard threshold assumptions, leading to better haze removal and image quality in challenging conditions.

Findings

Enhanced image contrast and visibility in hazy outdoor scenes.

Reduced noise and improved exposure in dark regions.

Significant enhancement of textures and edges in processed images.

Abstract

Image contrast enhancement for outdoor vision is important for smart car auxiliary transport systems. The video frames captured in poor weather conditions are often characterized by poor visibility. Most image dehazing algorithms consider to use a hard threshold assumptions or user input to estimate atmospheric light. However, the brightest pixels sometimes are objects such as car lights or streetlights, especially for smart car auxiliary transport systems. Simply using a hard threshold may cause a wrong estimation. In this paper, we propose a single optimized image dehazing method that estimates atmospheric light efficiently and removes haze through the estimation of a semi-globally adaptive filter. The enhanced images are characterized with little noise and good exposure in dark regions. The textures and edges of the processed images are also enhanced significantly.