# Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior

## TL;DR

This paper introduces a multiple linear regression model based on Dark Channel Prior to improve haze removal, achieving higher SSIM and PSNR scores and addressing DCP's limitations in bright and real-world hazy images.

## Contribution

It proposes a novel regression-based optimization of DCP for more accurate dehazing, trained on the RESIDE dataset, enhancing performance over existing algorithms.

## Key findings

- Highest SSIM value among compared methods
- PSNR surpasses most state-of-the-art algorithms
- Effectively handles bright and real-world hazy images

## Abstract

Dark Channel Prior (DCP) is a widely recognized traditional dehazing algorithm. However, it may fail in bright region and the brightness of the restored image is darker than hazy image. In this paper, we propose an effective method to optimize DCP. We build a multiple linear regression haze-removal model based on DCP atmospheric scattering model and train this model with RESIDE dataset, which aims to reduce the unexpected errors caused by the rough estimations of transmission map t(x) and atmospheric light A. The RESIDE dataset provides enough synthetic hazy images and their corresponding groundtruth images to train and test. We compare the performances of different dehazing algorithms in terms of two important full-reference metrics, the peak-signal-to-noise ratio (PSNR) as well as the structural similarity index measure (SSIM). The experiment results show that our model gets highest SSIM value and its PSNR value is also higher than most of state-of-the-art dehazing algorithms. Our results also overcome the weakness of DCP on real-world hazy images