Enhancement of low light level images with regression methods

Jie Yang; Xinwei Jiang; Chunhong Pan; Cheng-Lin Liu

doi:10.1117/12.2281535

21 July 2017 Enhancement of low light level images with regression methods

Jie Yang, Xinwei Jiang, Chunhong Pan, Cheng-Lin Liu

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202Q (2017) https://doi.org/10.1117/12.2281535
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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

Citation Download Citation

Jie Yang, Xinwei Jiang, Chunhong Pan, and Cheng-Lin Liu "Enhancement of low light level images with regression methods", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202Q (21 July 2017); https://doi.org/10.1117/12.2281535

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