LLDE: ENHANCING LOW-LIGHT IMAGES WITH DIFFUSION MODEL

Limited generalization capability has been an unsolved issue in the domain of low-light image enhancement. Many models find enhancing out-of-distribution underexposed images challenging. In this work, we offer a fresh point of view on this issue. Our approach involves dividing the enhancement process into many small steps and performing them gradually. This method allows the model to acquire a more robust understanding of the data. To put this concept into practice, we proposed to adopt a diffusion model for low-light image enhancement, as its way of encoding the mapping between the source and target distributions fits our idea. Empirically, we show that our proposed model (LLDE) can outperform recent SOTAs quantitatively and visually. The code is publicly available at https://github.com/OoiXinPeng/LLDE.