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Content Provider | IET Digital Library |
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Author | Zhang, Liang Shen, Peiyi Peng, Xilu Zhu, Guangming Song, Juan Wei, Wei Song, Houbing |
Abstract | Images obtained under low-light conditions tend to have the characteristics of low-grey levels, high-noise levels, and indistinguishable details. Image degradation not only affects the recognition of images, but also influences the performance of the computer vision system. The low-light image enhancement algorithm based on the dark channel prior de-hazing technique can enhance the contrast of images effectively and can highlight the details of images. However, the dark channel prior de-hazing technique ignores the effects of noise, which leads to significant noise amplification after the enhancement process. In this study, a de-hazing-based simultaneous enhancement and noise reduction algorithm of are proposed by analysing the essence of the dark channel prior de-hazing technique and bilateral filter. First, the authors estimate the values of the initial parameters of the hazy image model by de-hazing technique. Then, they correct the parameters of the hazy image model alternately with the iterative joint bilateral filter. Experimental results indicate that the proposed algorithm can simultaneously enhance the low-light images and reduce noise effectively. The proposed algorithm could also perform quite well compared with the current common image enhancement and noise reduction algorithms in terms of the subjective visual effects and objective quality assessments. |
Starting Page | 840 |
Ending Page | 847 |
Page Count | 8 |
ISSN | 17519659 |
Volume Number | 10 |
e-ISSN | 17519667 |
Issue Number | Issue 11, Nov (2016) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/10/11 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2015.0844 |
Journal | IET Image Processing |
Publisher Date | 2016-06-06 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Computer Vision Computer Vision And Image Processing Technique Computer Vision System Dark Channel Prior De-hazing Technique Filtering Method in Signal Processing Filtering Theory Hazy Image Model High-noise Levels Image Denoising Image Enhancement Interpolation And Function Approximation Iterative Joint Bilateral Filter Iterative Method Low-grey Levels Noise Reduction Algorithm Numerical Analysis Objective Quality Assessments Optical, Image And Video Signal Processing Single Low-light Image Enhancement Subjective Visual Effect |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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