PSC diffusion: patch-based simplified conditional diffusion model for low-light image enhancement

Low-light image enhancement is pivotal for augmenting the utility and recognition of visuals captured under inadequate lighting conditions. Previous methods based on Generative Adversarial Networks (GAN) are affected by mode collapse and lack attention to the inherent characteristics of low-light im...

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Bibliographic Details
Published in:Multimedia systems 2024-08, Vol.30 (4), Article 187
Main Authors: Wan, Fei, Xu, Bingxin, Pan, Weiguo, Liu, Hongzhe
Format: Article
Language:English
Subjects:
Attention
Computer Communication Networks
Computer Graphics
Computer Science
Cryptology
Data Storage Representation
Diffusion rate
Feature maps
Generative adversarial networks
Image enhancement
Image processing
Image quality
Multimedia Information Systems
Multiplication
Noise prediction
Operating Systems
Parameters
Regular Paper
Regularization
Sampling
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Summary:Low-light image enhancement is pivotal for augmenting the utility and recognition of visuals captured under inadequate lighting conditions. Previous methods based on Generative Adversarial Networks (GAN) are affected by mode collapse and lack attention to the inherent characteristics of low-light images. This paper propose the Patch-based Simplified Conditional Diffusion Model (PSC Diffusion) for low-light image enhancement due to the outstanding performance of diffusion models in image generation. Specifically, recognizing the potential issue of gradient vanishing in extremely low-light images due to smaller pixel values, we design a simplified U-Net architecture with SimpleGate and Parameter-free attention (SimPF) block to predict noise. This architecture utilizes parameter-free attention mechanism and fewer convolutional layers to reduce multiplication operations across feature maps, resulting in a 12–51% reduction in parameters compared to U-Nets used in several prominent diffusion models, which also accelerates the sampling speed. In addition, preserving intricate details in images during the diffusion process is achieved through employing a patch-based diffusion strategy, integrated with global structure-aware regularization, which effectively enhances the overall quality of the enhanced images. Experiments show that the method proposed in this paper achieves richer image details and better perceptual quality, while the sampling speed is over 35% faster than similar diffusion model-based methods.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-024-01391-z