Share Email Print
cover

Proceedings Paper

Enhancement of low light level images with regression methods
Author(s): Jie Yang; Xinwei Jiang; Chunhong Pan; Cheng-Lin Liu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The enhancement of Low Light Level Images (LLLIs) is challenging, due to their poor brightness and low contrast. Traditional enhancement methods fail to perform satisfactorily when applying to LLLIs. In this paper, we formulate the LLLI enhancement as a regression problem: the regressor maps patches of input image to enhanced patches, and the regression function is estimated by learning from sample images. We implemented two efficient regression methods based on piecewise linear regression: locally linear regression and random forest (RF). Meanwhile, we designed a new split function considering reconstruction error for random forest method. Experimental results on an open dataset and practical LLLIs demonstrate the effectiveness of our methods. The RF regression method performs superiorly in both enhancement quality and computation efficiency

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202Q (21 July 2017); doi: 10.1117/12.2281535
Show Author Affiliations
Jie Yang, Institute of Automation (China)
Xinwei Jiang, Institute of Automation (China)
Chunhong Pan, Institute of Automation (China)
Cheng-Lin Liu, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top