Abstract
Aiming at the uneven distribution of haze concentration and color imbalance in haze weather images, a natural hazy image enhancement method which combines with multilayer fusion and chunk-based is proposed. Based on the atmospheric physical model, the detail and base layers of scene images can be extracted using multilayer decomposition and nonlinear mapping function. Iterative Box Filter can improve the accuracy of ambient light selection and avoid the imbalance of ambient light estimation. The image is segmented into blocks, and the block images are processed by the same operations which are multilayer decomposition and nonlinear mapping function to obtain the detail maps of block images. By splicing these detail maps into the whole detail maps and combining with the dark channel priori, a good transmittance estimation map can be obtained. Throughout the experiment, the guide image filter is utilized to preserve the edges of the image and color correction is added to the model. The experiment results demonstrate that our proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons.
Highlights
Under natural conditions, clouds, fog, haze and other substances will form a complex lighting environment, which will lead to contrast reduction, blurring, color pollution and other problems and seriously affect the application of computer vision systems. [1], [2] eliminating the influence of sub-stances such as fog on image quality is a research hotspot in the field of computer image processing.There are two mainstream types of methods for dehazing algorithms which are restoration-based and enhancementbased image processing
Enhancement-based image dehazing currently mainly includes a series of algorithms derived from the Retinex method and histogram equalization method
The processing method of using Multi-scale Retinex method to obtain multiple low-frequency images by using multiple Gaussian filtering has a good reference in image enhancement
Summary
Clouds, fog, haze and other substances will form a complex lighting environment, which will lead to contrast reduction, blurring, color pollution and other problems and seriously affect the application of computer vision systems. [1], [2] eliminating the influence of sub-stances such as fog on image quality is a research hotspot in the field of computer image processing. Zhang et al [5] enhanced the global contrast, detail information, and color restoration with multi-scale Gaussian kernels and MSRCR He used the guided filter twice to reduce the noise apparently. According to the literatures in recent years, He et al [1] analyzed a slew of pictures and proposed a dark channel prior algorithm It can obtain the depth information of the image through dark channel to estimate the transmittance, which becomes a classic algorithm in image enhancement algorithm. Gong et al [16] designed a box filter to preserve both edge and corner with sub-window regression, which proved that this is a highly efficient method Combining these excellent ideas, we do some new work based on the atmospheric physical model. The whole process consists of four parts: multilayer decomposition, ambient light estimation, transmittance estimation and image color correction
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