Restoration by Generation with Constrained Priors

• URL: http://arxiv.org/abs/2312.17161v2
• Date: Sat, 1 Jun 2024 07:03:52 GMT
• Title: Restoration by Generation with Constrained Priors
• Authors: Zheng Ding, Xuaner Zhang, Zhuowen Tu, Zhihao Xia,
• Abstract summary: We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise. We show superior performances on multiple real-world restoration datasets in preserving identity and image quality. This approach allows us to produce results that accurately preserve high-frequency details, which previous works are unable to do.
• Score: 25.906981634736795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The inherent generative power of denoising diffusion models makes them well-suited for image restoration tasks where the objective is to find the optimal high-quality image within the generative space that closely resembles the input image. We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise. Our method is based on the observation that the space of a generative model needs to be constrained. We impose this constraint by finetuning the generative model with a set of anchor images that capture the characteristics of the input image. With the constrained space, we can then leverage the sampling strategy used for generation to do image restoration. We evaluate against previous methods and show superior performances on multiple real-world restoration datasets in preserving identity and image quality. We also demonstrate an important and practical application on personalized restoration, where we use a personal album as the anchor images to constrain the generative space. This approach allows us to produce results that accurately preserve high-frequency details, which previous works are unable to do. Project webpage: https://gen2res.github.io.

Related papers

• Towards Unsupervised Blind Face Restoration using Diffusion Prior [12.69610609088771]
  Blind face restoration methods have shown remarkable performance when trained on large-scale synthetic datasets with supervised learning. These datasets are often generated by simulating low-quality face images with a handcrafted image degradation pipeline. In this paper, we address this issue by using only a set of input images, with unknown degradations and without ground truth targets, to fine-tune a restoration model. Our best model also achieves the state-of-the-art results on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-10-06T20:38:14Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
  We propose a training-free method, dubbed DiffUHaul, for the object dragging task. We first apply attention masking in each denoising step to make the generation more disentangled across different objects. In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
  arXiv  Detail & Related papers  (2024-06-03T17:59:53Z)
• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [88.04880564539836]
  Concerns have arisen regarding potential misuse related to images generated by latent generative models. We propose a latent inversion based method called LatentTracer to trace the generated images of the inspected model. Our experiments show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-05-22T05:33:47Z)
• Active Generation for Image Classification [45.93535669217115]
  We propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation.
  arXiv  Detail & Related papers  (2024-03-11T08:45:31Z)
• InvGAN: Invertible GANs [88.58338626299837]
  InvGAN, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model. This allows us to perform image inpainting, merging, and online data augmentation.
  arXiv  Detail & Related papers  (2021-12-08T21:39:00Z)
• Restormer: Efficient Transformer for High-Resolution Image Restoration [118.9617735769827]
  convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data. Transformers have shown significant performance gains on natural language and high-level vision tasks. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks.
  arXiv  Detail & Related papers  (2021-11-18T18:59:10Z)
• Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization [72.65828901909708]
  Controllable person image generation aims to produce realistic human images with desirable attributes. We introduce a novel Spatially-Adaptive Warped Normalization (SAWN), which integrates a learned flow-field to warp modulation parameters. We propose a novel self-training part replacement strategy to refine the pretrained model for the texture-transfer task.
  arXiv  Detail & Related papers  (2021-05-31T07:07:44Z)
• Perceptual Image Restoration with High-Quality Priori and Degradation Learning [28.93489249639681]
  We show that our model performs well in measuring the similarity between restored and degraded images. Our simultaneous restoration and enhancement framework generalizes well to real-world complicated degradation types.
  arXiv  Detail & Related papers  (2021-03-04T13:19:50Z)
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation [181.08127307338654]
  This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
  arXiv  Detail & Related papers  (2020-03-30T17:45:07Z)

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.