BlurDM: A Blur Diffusion Model for Image Deblurring
Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The source code is available at https://github.com/Jin-Ting-He/BlurDM.
6931a6342d1e5b0a7d84db59
Jin-Ting He
6931a6342d1e5b0a7d84db5a
Fu-Jen Tsai
6931a6342d1e5b0a7d84db5b
Yan-Tsung Peng
6931a6342d1e5b0a7d84db5c
Min-Hung Chen
claimed_verified
2025-12-04T16:09:09.803Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64ae22dd1aee69ece065cdcd/JG7QaHIrr4i2k4uwR4pZK.png
Min-Hung Chen
cmhungsteve
user
cmhungsteve
6931a6342d1e5b0a7d84db5d
Chia-Wen Lin
6931a6342d1e5b0a7d84db5e
Yen-Yu Lin
BlurDM: A Blur Diffusion Model for Image Deblurring
Diffusion models show promise for dynamic scene deblurring; however, existing studies often fail to leverage the intrinsic nature of the blurring process within diffusion models, limiting their full potential. To address it, we present a Blur Diffusion Model (BlurDM), which seamlessly integrates the blur formation process into diffusion for image deblurring. Observing that motion blur stems from continuous exposure, BlurDM implicitly models the blur formation process through a dual-diffusion forward scheme, diffusing both noise and blur onto a sharp image. During the reverse generation process, we derive a dual denoising and deblurring formulation, enabling BlurDM to recover the sharp image by simultaneously denoising and deblurring, given pure Gaussian noise conditioned on the blurred image as input. Additionally, to efficiently integrate BlurDM into deblurring networks, we perform BlurDM in the latent space, forming a flexible prior generation network for deblurring. Extensive experiments demonstrate that BlurDM significantly and consistently enhances existing deblurring methods on four benchmark datasets. The source code is available at https://github.com/Jin-Ting-He/BlurDM.
Blur Diffusion Model (BlurDM) integrates blur formation into diffusion for image deblurring, enhancing deblurring methods by simultaneously denoising and deblurring images.
diffusion models Blur Diffusion Model BlurDM motion blur dual-diffusion dual denoising deblurring latent space prior generation network