2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-1.12
Paper Title Subband Adaptive Enhancement of Low Light Images Using Wavelet-Based Convolutional Neural Networks
Authors Zhe Ji, Cheolkon Jung, Xidian University, China
SessionTEC-1: Restoration and Enhancement 1
LocationArea G
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.