Low-Light Image Enhancement Based on RAW Domain Image

It is easier to model the noise in RAW image data (RAW images) than in RGB images because RAW images provide the original data without the nonlinear mapping of camera ISP. Therefore, this paper proposes a low-light image quality enhancement method in the RAW domain for clear and high-quality images. Firstly, the RAW image captured by the camera sensor is linearly interpolated to obtain a four-channel RGGB color image. Secondly, we generate more RAW images by applying different exposure levels on this RGGB image. Finally, we develop a neural network to learn the mapping between the RAW images with different exposure levels and reference images. Our proposed neural network adopts the autoencoder structure where the channel attention module is incorporated to extract and enhance the latent features of images. In the training, we design a new loss function that combines both structural similarity loss and gradient descent loss to guide the network to produce high quality images with high structural similarity and color relevance to the reference image. The proposed method is trained and tested on the See-In-the-Dark (SID) dataset. It achieves average 29.738 0 dB PSNR and 30.233 4 dB MPSNR, which outperforms state-of-the-art methods EnlightenGAN, Zero-DCE, SID, Residual, and ALEN. In terms of subjective quality, the images enhanced by the proposed method have no obvious noise and color spot artifacts, looking more visually pleasing and appealing than the images from previous methods. © 2023 Institute of Computing Technology. All rights reserved.