This work proposes a novel training-sampling approach for single-image multi-scale diffusion models that enables consistent and joint sampling of image sequences and introduces a Laplacian-recomposition sampling algorithm to enforce multi-scale consistency.
Observation Operator Diffusion is proposed, a unified framework that aligns both the supervision trajectory and feature refinement with the intrinsic recovery order of image structures and introduces GL-CoDA, a decoder that injects scale-specific Gaussian-Lanczos observations across decoding stages for coarse-to-fine f...
Shaojie Guo, Li-Chen Ma, Haoyang Tong et al.· 0 citations
We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collections, or object segmentation masks, we show that a single image can suffice to learn a meaningful object model in a completely bottom-up fas...
Syrine Kalleli, Alexei A. Efros, Mathieu Aubry· 0 citations
This work repurposes pretrained video generative models as a unified and data-efficient framework for geometry estimation, formulated innovatively as a next-frames prediction task, and inherits naturally structured knowledge and richer priors from the video model, enabling more data efficient and effective learning of...
Haosen Yang, Jifei Song, Zhensong Zhang et al.· 1 citation
This work proposes a perceptually regularized diffusion framework that incorporates prior knowledge through perceptual-loss-based regularization, improving training convergence and encouraging the recovery of meaningful image features.
Chuxiang Wang, Pavithra Venkatachalapathy, Ying Liang et al.· 0 citations
PixRestore is presented, a VAE-free pixel-space Diffusion Transformer (DiT) for UIR, where the diffusion backbone is trained entirely from scratch, without relying on T2I pretraining.
Ling-Chen Sun, Rong-Yuan Wu, Xiangtao Kong et al.· 0 citations
Blind image deconvolution, the task of recovering a sharp image from a blurred observation with an unknown kernel, remains a challenging ill-posed inverse problem. While deep learning methods trained on large-scale datasets have achieved remarkable performance, their application is often limited by the availability of...
Ting-Ting Wu, Pei-Xuan Song, Wu-Fan Zhao et al.· IEEE Transactions on Computa...· 0 citations
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