A wavelet-based diffusion model for low-light image enhancement in raw space

Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image information and thus have more space for image processing. Existing methods often struggle to approximate real images in terms of image detail enhancement and color restoration. Drawing inspiration from the latest advancements in denoising diffusion models, we introduce an innovative framework that leverages wavelet transform and diffusion models to tackle the low light image enhancement challenge in Raw space. In our method, the input low-light image is first decomposed into different frequency sub-bands by discrete wavelet transform (DWT) from both image and feature levels. Then a redesigned diffusion model is trained to adaptively denoise and enhance the different frequency sub-bands. In order to achieve better results in human eye perception, we also improved the loss function during training process. Finally, the enhanced result of the low-light image is finally reconstructed through inverse wavelet transform (IWT) and subsequent processing, transitioning from Raw space to sRGB space. Extensive experiments on SID and ELD datasets show that our refined method outperforms several existing methods, delivering superior performance both quantitatively and qualitatively.