Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

TL;DR

This paper introduces a multiple linear regression dehazing model based on Dark Channel Prior, trained on synthetic data, which improves image quality and object detection accuracy in hazy environments, relevant for autonomous driving.

Contribution

The authors develop a novel regression-based dehazing model and a synthetic hazy dataset for training, enhancing image clarity and object detection in hazy conditions.

Findings

The proposed model outperforms conventional dehazing algorithms in image quality.

Synthetic haze generation improves object detection accuracy.

Both dehazing and synthetic data training significantly boost Mask R-CNN performance.

Abstract

Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior. Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior. To increase object detection accuracy in the hazy environment, the authors further present an algorithm to build a synthetic hazy COCO training dataset by generating the artificial haze to the MS COCO training dataset. The experimental results demonstrate that the proposed model obtains higher image quality and shares more similarity with ground truth images than most conventional pixel-based dehazing algorithms and neural network based haze-removal models. The authors also evaluate the mean average precision of Mask R-CNN when training the network with synthetic hazy COCO training dataset and preprocessing test hazy dataset by removing the haze with the proposed dehazing model. It turns out that both approaches can increase the object detection accuracy significantly and outperform most existing object detection models over hazy images.