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Dark Channel Prior (DCP) is a widely recognized traditional dehazingalgorithm. However, it may fail in bright region and the brightness of therestored image is darker than hazy image. In this paper, we propose aneffective method to optimize DCP. We build a multiple linear regressionhaze-removal model based on DCP atmospheric scattering model and train thismodel with RESIDE dataset, which aims to reduce the unexpected errors caused bythe rough estimations of transmission map t(x) and atmospheric light A. TheRESIDE dataset provides enough synthetic hazy images and their correspondinggroundtruth images to train and test. We compare the performances of differentdehazing algorithms in terms of two important full-reference metrics, thepeak-signal-to-noise ratio (PSNR) as well as the structural similarity indexmeasure (SSIM). The experiment results show that our model gets highest SSIMvalue and its PSNR value is also higher than most of state-of-the-art dehazingalgorithms. Our results also overcome the weakness of DCP on real-world hazyimages

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