Subband Adaptive Enhancement Of Low Light Images Using Wavelet-Based Convolutional Neural Networks

Images captured in low light condition have a narrow dynamic range with a dark tone, which are seriously degraded by noise due to the low signal-to-noise ratio (SNR). Discrete wavelet transform (DWT) is invertible and thus is able to decompose an image into subbands without information loss minimizing redundancy. In this paper, we propose subband adaptive enhancement of low light images using wavelet-based convolutional neural networks. We adopt DWT to achieve joint contrast enhancement and noise reduction. We combine DWT with convolutional neural networks (CNNs), i.e. wavelet-based CNN, to facilitate subband adaptive processing. First, we decompose the input image into LL, LH, HL, and HH subbands to get low and high frequency components. Second, we perform contrast enhancement for LL subband and noise reduction for LH, HL and HH subbands. Finally, we perform refinement to enhance image details. Experimental results show that the proposed method enhances low light images while successfully removing noise as well as outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.