Abstract

In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise. Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF). NLF is used to get a noise aware histogram which prevents noise amplification, and we use the noise aware histogram in contrast enhancement. However, the increase of intensity by contrast enhancement reduces the visibility threshold, which makes noise visible by human eyes. Second, we utilize a just noticeable difference (JND) model from luminance adaptation to suppress noise based on human visual perception. Experimental results show that the proposed method successfully enhances contrast in low light images while minimizing noise amplification.

Paper

DOI:https://doi.org/10.1109/ICASSP.2017.7952502

Citation:
Haonan Su, Cheolkon Jung, “Low Light Image Enhancement Based on Two-Step Noise Suppression”Proc. IEEE ICASSP, pp. 1977-1981, 2017.

Test Images and Executable Files:

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Motivation

Histogram-based contrast enhancement of low light images often causes severe noise amplification and over enhancement without considering noise characteristics. Two reasons lead to this problem as follows:

1)JND (Human visual system): Low light images contain large flat regions with serious noise, but low visual sensitive of dark intensity reduces the noise visibility and makes the noise invisible as shown in Fig.2(a).

2) Noise characteristics: Noise level becomes larger in low intensity (0-10), and decreases rapidly as intensity increases as shown in Fig. 2(b).

Results

First, we have utilized noise level function (NLF) to obtain the noise aware histogram considering image content and noise level, and performed noise aware contrast enhancement based on the histogram. Second, we have employed the JND model from luminance adaptation to suppress noise based on human visual perception. As shown in Fig.3, the proposed method successfully enhances contrast in low light images while minimizing noise amplification.

Reference

[1] A. Loza, D. R. Bull, and P. R. Hill et al, “Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients,” Digital Signal Processing, vol. 23, no. 6, pp. 1856–1866, Jun. 2013.

[2] A.R. Rivera, B. Ryu, and O.Chae, “Content-aware dark image enhancement through channel division,” IEEE transactions on image processing, vol. 21, no. 9, pp. 3967–3980, Sep. 2012.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61271298) and the International S&T Cooperation Program of China (No. 2014DFG12780)