Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
This work addresses image quality issues in computer vision applications like photography and surveillance, but it is incremental as it builds upon the established DCP method.
The paper tackles the problem of image dehazing by optimizing the Dark Channel Prior (DCP) algorithm, which often fails in bright regions and produces darker images. The proposed multiple linear regression model achieves the highest SSIM value and a higher PSNR than most state-of-the-art algorithms, overcoming DCP's weaknesses on real-world images.
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