Image Restoration via Diffusion Models with Dynamic Resolution

• URL: http://arxiv.org/abs/2605.14267v1
• Date: Thu, 14 May 2026 02:11:47 GMT
• Title: Image Restoration via Diffusion Models with Dynamic Resolution
• Abstract summary: This work proposes projecting data into lower-dimensional subspaces using dynamic resolution DMs to accelerate the inference process.<n>We first fine-tune pre-trained DMs for dynamic resolution priors and adapt DPS and DAPS, which are two widely used pixel-space methods for general image restoration tasks.<n>Given the favorable inference speed and reconstruction fidelity of SubDAPS, we introduce an enhanced variant termed SubDAPS++ to further boost both reconstruction efficiency and quality.
• Score: 19.445391508424667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within the high-dimensional pixel space, resulting in high computational overhead. While methods based on latent DMs seek to alleviate this issue by utilizing the compressed latent space of a variational autoencoder, they require repeated encoder-decoder inference. This introduces significant additional computational burdens, often resulting in runtime performance that is even inferior to that of their pixel-space counterparts. To mitigate the computational inefficiency, this work proposes projecting data into lower-dimensional subspaces using dynamic resolution DMs to accelerate the inference process. We first fine-tune pre-trained DMs for dynamic resolution priors and adapt DPS and DAPS, which are two widely used pixel-space methods for general image restoration tasks, into the proposed framework, yielding methods we refer to as SubDPS and SubDAPS, respectively. Given the favorable inference speed and reconstruction fidelity of SubDAPS, we introduce an enhanced variant termed SubDAPS++ to further boost both reconstruction efficiency and quality. Empirical evaluations across diverse image datasets and various restoration tasks demonstrate that the proposed methods outperform recent DM-based approaches in the majority of experimental scenarios. The code is available at https://github.com/StarNextDay/SubDAPS.git.

Related papers

• AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution [14.2500092850787]
  Diffusion models (DMs) have shown promising results on single-image super-resolution and other image-to-image translation tasks. We propose AdaDiffSR, a DMs-based super-resolution pipeline with dynamic timesteps sampling strategy (DTSS) Our experiments show that our AdaDiffSR achieves comparable performance over current state-of-the-art DMs-based SR methods.
  arXiv  Detail & Related papers  (2024-10-23T10:29:18Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems [7.043932618116216]
  vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems) is a novel Deep Learning (DL)-based method for solving ill-posed inverse problems arising in Medical Imaging (MI) For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.
  arXiv  Detail & Related papers  (2023-09-18T17:26:22Z)
• Deep Physics-Guided Unrolling Generalization for Compressed Sensing [8.780025933849751]
  Deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction. We find the intrinsic defect of this emerging paradigm, widely implemented by deep algorithm-unrolled networks. A novel deep $textbfP$hysics-guided untextbfR$olled recovery is proposed.
  arXiv  Detail & Related papers  (2023-07-18T03:37:10Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Generative Diffusion Prior for Unified Image Restoration and Enhancement [62.76390152617949]
  Existing image restoration methods mostly leverage the posterior distribution of natural images. We propose the Generative Diffusion Prior (GDP) to effectively model the posterior distributions in an unsupervised sampling manner. GDP utilizes a pre-train denoising diffusion generative model (DDPM) for solving linear inverse, non-linear, or blind problems.
  arXiv  Detail & Related papers  (2023-04-03T16:52:43Z)
• Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation [2.5556910002263984]
  Score-based diffusion models (SBDM) have emerged as state-of-the-art approaches for image generation. This paper develops SBDMs in the infinite-dimensional setting, that is, we model the training data as functions supported on a rectangular domain. We demonstrate how to overcome two shortcomings of current SBDM approaches in the infinite-dimensional setting.
  arXiv  Detail & Related papers  (2023-03-08T18:10:10Z)
• Direct Dense Pose Estimation [138.56533828316833]
  Dense human pose estimation is the problem of learning dense correspondences between RGB images and the surfaces of human bodies. Prior dense pose estimation methods are all based on Mask R-CNN framework and operate in a top-down manner of first attempting to identify a bounding box for each person. We propose a novel alternative method for solving the dense pose estimation problem, called Direct Dense Pose (DDP)
  arXiv  Detail & Related papers  (2022-04-04T06:14:38Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.