Abstract
In this article, we present a new method of dehazing based on the Koschmieder model, which aims to restore an image that has been affected by haze. The difficulty is to improve the estimation of the transmission and the atmospheric light that generally suffer from the nonhomogeneity and the random variability of the environment. The keypoint is to enhance the dehazing of very bright regions of the image in order to improve the treatment of the sky that is often overestimated or underestimated compared to the rest of the scene. The approach proposed in this paper is based on two main contributions: 1. an L0 gradient optimization function weighted by a set of Gaussian filters and based on an iterative algorithm for optimization convergence. Unlike the existing methods using a single value of the atmospheric light for the whole image, our method uses a set of values neighboring an initial estimated value. The fusion is then applied based on Laplacian and Gaussian pyramids to combine all the relevant information from the set of images constructed from atmospheric lights and improves the contrast to recover the colors of the sky without any artifacts. Finally, the results are validated by three criteria: an autocorrelation score (ZNCC), a similarity measure (SSIM) and a visual criterion. The experiments carried out on two datasets show that our approach allows a better dehazing of the images with higher SSIM and ZNCC measurements but also with better visual quality.
Highlights
Image restoration is one of the fundamental issues in image processing taken under degraded conditions, such as fog or turbidity in underwater environments
In order to improve the performance of these methods, we propose to fuse several values of the atmospheric light to better estimate the haze, or the transmission, and obtain results closer to reality as the atmospheric light appears twice in the diffusion formula
We consider the method to be efficient and likely to have a good visual rendering if it achieves a better score on both SSIM and ZNCC, usually greater than 0.65 for each criterion
Summary
Image restoration is one of the fundamental issues in image processing taken under degraded conditions, such as fog or turbidity in underwater environments. Among the first methods to have dealt with this problem, He’s [5] method is based on the dark channel (DCP) which is refined with the Laplacian matrix to obtain the transmission. This solution is robust but very expensive in calculation time and it is based on a statistical observation that claims that in a small window of the image there is at least one dark channel, which is not always true and does not lead to a good estimation of the transmission. The weak points of these methods are the calculation time, which is quite high and generates several errors in areas where the image
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
