Abstract
In hazy environments, image quality is degraded by haze and the degraded photos have reduced visibility, making the less vivid and visually attractive. This paper proposes a method for recovering image information from a single hazy image. The dark channel prior algorithm tends to underestimate the transmission of bright areas. To address this problem, an improved dehazing algorithm is proposed in this paper. Assuming that intensity in a dark channel affected by haze produces the same offset, the expected value of the dark channel of a hazy image is used as an approximation of this offset to correct the transmission. However, this correction may neglect scene difference and affect the clarity of the recovered images. Therefore, a weighted residual map is used to enhance contrast and recover more information. Experimental results demonstrate that our algorithm can effectively lessen color oversaturation and restore images with enhanced details. This algorithm provides a more accurate transmission estimation method that can be used with a weighted residual map to eliminate haze and improve contrast.
Highlights
Aerosols in the air scatter light into the atmosphere
We do an ablation study to demonstrate the effectiveness of our offset correction and the weighted residual map
We proposed an improved dehazing algorithm based on the dark channel prior
Summary
Aerosols in the air scatter light into the atmosphere This scattering impairs the direct transmission of scene radiance and degrades image quality, especially on hazy days [1]. These degraded images usually have low contrast and saturation, loss of detail, and hue shift, thereby affecting visual effects and subsequently image processing. Based on the atmospheric scattering model describing the attenuation and distribution of light through aerosols, a hazy image is described as a convex combination of scene radiance and atmospheric light, and the coefficients of this equation are determined by the scene transmission of each pixel in the image. Some methods use additional information about the scene, such as multiple images taken under diverse conditions [2], polarization angle [3], or geometric features of the scene [4], to determine transmission and obtain haze-free images
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