A Cnn-Transformer Network Based Snr Guided High Frequency Reconstruction for Low Light Image Enhancement

Photographs taken in low-light conditions have a low signal-to-noise ratio and impaired visual quality. We observe that low-light images exhibit a lower signal-to-noise ratio, resulting in a mixture of fine details, textures, and noise, making it challenging to reconstruct small-scale textures in the image. Inspired by this observation, we propose a SNR-guided CNN-Transformer network for high frequency restoration during low light image enhancement. The proposed method first decomposes image into high-frequency and low frequency components by image decomposition module. The low-frequency image is processed by a trainable Low Frequency SNR Perception (LFSP) module, resulting in excellent denoising performance and generating SNR-enhanced images with clearer edge contours. Guided by the low-frequency SNR feature maps, the details and textures of the high-frequency components are enhanced using a combination of transformer networks and convolutional networks, thereby compensating the detail distortions in the high frequency components of the image. The subjective and objective experiments demonstrate that our proposed method outperforms existing approaches in terms of detail and structure preservation.