Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function
This is an incremental improvement for atmospheric optics applications that rely on clear dust images.
The paper tackles the problem of enhancing sand dust images that suffer from poor visibility and color shifts by proposing a three-phase model combining color correction, haze removal, and contrast/brightness enhancement. The results show it outperforms current studies in removing red and yellow casts and providing high-quality images.
Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and new membership function to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a new membership function to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.