Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild

• URL: http://arxiv.org/abs/2302.07864v1
• Date: Wed, 15 Feb 2023 18:56:06 GMT
• Title: Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
• Abstract summary: This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing a new state-of-the-art. We advocate self-supervised training with a combination of composite, parameterized degradations for self-supervised training, and noise-conditioing augmentation during training and testing. With these innovations, a large-scale convolutional architecture, and large-scale datasets, SR3+ greatly outperforms SR3.
• Score: 2.190237353315235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks. Despite this success, they have not outperformed state-of-the-art GAN models on the more challenging blind super-resolution task, where the input images are out of distribution, with unknown degradations. This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing a new state-of-the-art. To this end, we advocate self-supervised training with a combination of composite, parameterized degradations for self-supervised training, and noise-conditioing augmentation during training and testing. With these innovations, a large-scale convolutional architecture, and large-scale datasets, SR3+ greatly outperforms SR3. It outperforms Real-ESRGAN when trained on the same data, with a DRealSR FID score of 36.82 vs. 37.22, which further improves to FID of 32.37 with larger models, and further still with larger training sets.

Related papers

• InfScene-SR: Spatially Continuous Inference for Arbitrary-Size Image Super-Resolution [3.6762434952581713]
  InfScene-SR is a framework enabling spatially continuous super-resolution for large, arbitrary scenes.<n>We adapt the iterative refinement process of diffusion models with a novel guided and variance-corrected fusion mechanism.
  arXiv  Detail & Related papers  (2026-02-23T11:34:59Z)
• Enhanced Semantic Extraction and Guidance for UGC Image Super Resolution [18.058473238611725]
  We propose a novel approach to image super-resolution by integrating semantic guidance into a diffusion framework. Our method addresses the inconsistency between degradations in wild and synthetic datasets. Our model won second place in the CVIRE 2025 Short-form Image Super-Resolution Challenge.
  arXiv  Detail & Related papers  (2025-04-14T05:26:24Z)
• Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models [33.519892081718716]
  We propose aligning the latent space with pre-trained vision foundation models when training the visual tokenizers. Our proposed VA-VAE significantly expands the reconstruction-generation frontier of latent diffusion models. We build an enhanced DiT baseline with improved training strategies and architecture designs, termed LightningDiT.
  arXiv  Detail & Related papers  (2025-01-02T18:59:40Z)
• Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors [75.24313405671433]
  Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. We introduce a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module specifically for SR.
  arXiv  Detail & Related papers  (2024-09-25T16:15:21Z)
• DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF [50.458896463542494]
  DiSR-NeRF is a diffusion-guided framework for view-consistent super-resolution (SR) NeRF. We propose Iterative 3D Synchronization (I3DS) to mitigate the inconsistency problem via the inherent multi-view consistency property of NeRF.
  arXiv  Detail & Related papers  (2024-04-01T03:06:23Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• DFU: scale-robust diffusion model for zero-shot super-resolution image generation [15.689418447376587]
  We present a novel deep-learning architecture, Dual-FNO UNet (DFU), which approximates the score operator by combining both spatial and spectral information at multiple resolutions. We propose a fine-tuning strategy to further enhance the zero-shot super-resolution image-generation capability of our model, leading to a FID of 11.3 at 1.66 times the maximum training resolution on FFHQ.
  arXiv  Detail & Related papers  (2023-11-30T23:31:33Z)
• Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction [77.69363640021503]
  3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. We present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects.
  arXiv  Detail & Related papers  (2023-04-13T17:59:01Z)
• Image Super-Resolution via Iterative Refinement [53.57766722279425]
  SR3 is an approach to image Super-Resolution via Repeated Refinement. It adapts probabilistic denoising diffusion models to conditional image generation. It exhibits strong performance on super-resolution tasks at different magnification factors.
  arXiv  Detail & Related papers  (2021-04-15T17:50:42Z)
• Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation [6.14297871633911]
  In real-world single image super-resolution (SISR) task, the low-resolution image suffers more complicated degradations. In this paper, we focus on the camera-screen degradation and build a real-world dataset (Cam-ScreenSR) We propose a joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is trained to model the degradation and produces more various of LR images. Then the dual residual channel attention network (DuRCAN) learns to recover the SR image.
  arXiv  Detail & Related papers  (2020-08-01T07:10:13Z)
• Characteristic Regularisation for Super-Resolving Face Images [81.84939112201377]
  Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data. This renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. We formulate a method that joins the advantages of conventional SR and UDA models.
  arXiv  Detail & Related papers  (2019-12-30T16:27:24Z)

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.