Paper Info
Title
Low-light image enhancement by diffusion pyramid with residuals
Abstract
With the advancement of the camera-related technology in mobile devices, the vast amount of photos have been taken and shared in our daily life. However, many users still have unsatisfactory experiences with low-visible photos, which are frequently acquired under complicated real-world environments. In this paper, a novel yet simple method for low-light image enhancement has been proposed without any learning procedure. The key idea of the proposed method is to estimate properties of the scene illumination both in global and local manner by exploiting the diffusion pyramid with residuals. Specifically, the residual of each scale level in the diffusion pyramid is combined with the corresponding input. This restored result efficiently highlights local details across different scale spaces, thus it is helpful for preserving the boundary of illuminations. By conducting max-pooling with restored results from different levels of the diffusion pyramid, which are resized to the original resolution, the illumination component is accurately inferred from a given image. Compared to recent learning-based approaches, one important advantage of the proposed method is to effectively avoid the overfitting problem to the specific training dataset. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method for low-light image enhancement in real-world scenarios.
Year
DOI
Venue
2021
10.1016/j.jvcir.2021.103364
Journal of Visual Communication and Image Representation
Keywords
DocType
Volume
Low-light image enhancement,Scene illumination,Diffusion pyramid with residuals
Journal
81
ISSN
Citations 
PageRank 
1047-3203
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Wonjun Kim130126.50