Abstract
Low-light image enhancement is crucial for accurately interpreting images captured under low-lighting conditions. Existing low-light enhancement methods based on diffusion models have demonstrated effectiveness; however, they suffer from slow training processes and less structured guidance during optimization. We propose a navel patch-wise-based diffusion model, which introduces the low curvature reverse trajectory to ensure stable parameter optimization and uncertainty guidance in the diffusion training process. Specifically, we randomly select patches of varying sizes from the entire image and apply patch-wise optimization between the generated image and the ground truth to enforce a stable optimization path in the diffusion model. Additionally, within each patch-wise region, an uncertainty network estimates the uncertainty, which is then integrated as a weighting factor in the diffusion process to balance areas of abrupt change in the image. Experimental evaluations on various datasets demonstrate that our method achieves significant improvements, particularly in experiments with real-world images. These results indicate that the proposed patch-wise-based diffusion model enhancements are effective.
Published Version
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