Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function

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

Enhanced DCP filter for Real-World Hazy Scenes

Haze is an atmospheric phenomenon that considerably degrades the visibility of out- door scenes. This happens due to atmosphere particles that absorb and disperse the sunshine. This paper introduces a unique single image visibility restoration algorithm that enhances visibility of such corrupted pictures. A unique edge-preserving decomposition-based technique is prepared to estimate transmission map for a haze image. Therefore, haze removal algorithmic rule has been taken from Koschmiedars law that includes a quick replacement-variation approach to dehaze and denoise at the same time. The proposed technique Enhanced DCP Filter (EDCPF) initially estimates a transmission map employing a windows adaptive technique that supported the dark channel. Restoration of foggy images is an important issue for the de-weathering in computer vision. A new method has been introduced for estimating the optical transmission in hazy scenes. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scenes

A Color Image Database for Haze Model and Dehazing Methods Evaluation

International audienceOne of the major issues related to dehazing methods (single or multiple image based) evaluation is the absence of the haze-free image (ground-truth). This is also a problem when it concerns the validation of Koschmieder model or its subsequent dehazing methods. To overcome this problem, we created a database called CHIC (Color Hazy Image for Comparison), consisting of two scenes in controlled environment. In addition to the haze-free image, we provide 9 images of different fog densities. Moreover, for each scene, we provide a number of parameters such as local scene depth, distance from the camera of known objects such as Macbeth Color Checkers, their radiance, and the haze level through transmittance. All of these features allow the possibility to evaluate and compare between dehazing methods by using full-reference image quality metrics regarding the haze-free image, and also to evaluate the accuracy of the Koschmieder hazy image formation model