Single Image Restoration for Participating Media Based on Prior Fusion
This addresses image restoration for various participating media, offering a more general approach than prior work, though it appears incremental as it builds on existing priors and models.
The paper tackles the problem of restoring degraded images captured in participating media like fog or turbid water by proposing a method based on fusing local contrast and color priors, achieving demonstrated capabilities on underwater and foggy images with evaluation on a ground-truth dataset.
This paper describes a method to restore degraded images captured in a participating media -- fog, turbid water, sand storm, etc. Differently from the related work that only deal with a medium, we obtain generality by using an image formation model and a fusion of new image priors. The model considers the image color variation produced by the medium. The proposed restoration method is based on the fusion of these priors and supported by statistics collected on images acquired in both non-participating and participating media. The key of the method is to fuse two complementary measures --- local contrast and color data. The obtained results on underwater and foggy images demonstrate the capabilities of the proposed method. Moreover, we evaluated our method using a special dataset for which a ground-truth image is available.