L2‐norm‐based prior for haze‐removal from single image. Issue 5 (19th February 2016)

• Record Type: Journal Article
• Authors: Jha, Deepak Kumar; Gupta, Bhupendra; Lamba, Subir Singh
• Is Part Of: IET computer vision. Volume 10:Issue 5(2016)
• Page Start: 331
• Page End: 343
• Publication Date: 2016-02-19
• Subjects: statistical analysis -- image restoration; l2-norm-based prior -- haze-removal -- outdoor images -- airlight -- dark channel prior -- dark intensity pixels -- statistical technique -- airlight estimation -- single-input hazy image; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2014.0449
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Climatic and atmospheric phenomena – such as haze, fog and smoke – may lead to deterioration of quality and poor scenic clarity of outdoor images. In computer graphics, the authors can model these images as a linear combination of scene radiance, medium transmission and airlight. Several techniques have been proposed to remove the effects of haze from images using this model. The most effective approach for removing the haze effect from a single image is based on dark channel prior. Dark channel prior is based on statistical observation of outdoor images comprising some regions with dark intensity pixels. Here we propose a new l 2 ‐norm‐based prior to generate a dark channel in order to remove the haze from a single‐input image. The dark channel generated using this new prior is more robust and free from the block‐effect. We also propose a statistical technique for airlight estimation of a given image. The proposed technique for modifying the dark channel prior and the airlight estimation are robust techniques as compared with approaches detailed in currently available literature. By combining this modified dark channel and estimated airlight, the haze can be directly removed and a more accurate haze‐free image can be recovered from single‐input hazy image.