Light Source Point Cluster Selection Based Atmosphere Light Estimation
This work addresses a specific bottleneck in image defogging for computer vision applications, but it is incremental as it builds on existing atmosphere scattering models.
The paper tackles the problem of accurately estimating atmosphere light value in defogging algorithms to avoid chromatic distortion in restored images, and the result is a method that uses clustering statistics to produce more accurate atmosphere brightness vectors and light source locations, leading to improved subjective defogging effects and objective image quality indicators.
Atmosphere light value is a highly critical parameter in defogging algorithms that are based on an atmosphere scattering model. Any error in atmosphere light value will produce a direct impact on the accuracy of scattering computation and thus bring chromatic distortion to restored images. To address this problem, this paper propose a method that relies on clustering statistics to estimate atmosphere light value. It starts by selecting in the original image some potential atmosphere light source points, which are grouped into point clusters by means of clustering technique. From these clusters, a number of clusters containing candidate atmosphere light source points are selected, the points are then analyzed statistically, and the cluster containing the most candidate points is used for estimating atmosphere light value. The mean brightness vector of the candidate atmosphere light points in the chosen point cluster is taken as the estimate of atmosphere light value, while their geometric center in the image is accepted as the location of atmosphere light. Experimental results suggest that this statistics clustering method produces more accurate atmosphere brightness vectors and light source locations. This accuracy translates to, from a subjective perspective, more natural defogging effect on the one hand and to the improvement in various objective image quality indicators on the other hand.