Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models

• URL: http://arxiv.org/abs/2404.09732v1
• Date: Mon, 15 Apr 2024 12:34:21 GMT
• Title: Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
• Authors: Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön,
• Abstract summary: This work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR) Our base diffusion model is the image restoration SDE (IR-SDE)
• Score: 14.25759541950917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffusion models trained in specific datasets fail to recover images that have out-of-distribution degradations. To address this problem, this work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR). More specifically, all low-quality images are simulated with a synthetic degradation pipeline that contains multiple common degradations such as blur, resize, noise, and JPEG compression. Then we introduce robust training for a degradation-aware CLIP model to extract enriched image content features to assist high-quality image restoration. Our base diffusion model is the image restoration SDE (IR-SDE). Built upon it, we further present a posterior sampling strategy for fast noise-free image generation. We evaluate our model on both synthetic and real-world degradation datasets. Moreover, experiments on the unified image restoration task illustrate that the proposed posterior sampling improves image generation quality for various degradations.

Related papers

• Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance [11.983231834400698]
  No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. We introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images.
  arXiv  Detail & Related papers  (2024-11-26T12:48:47Z)
• Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding [67.57487747508179]
  Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. In this paper, we propose a novel multiple-in-one IR model that can effectively restore images with both single and mixed degradations.
  arXiv  Detail & Related papers  (2024-11-25T09:26:34Z)
• Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method [7.487270862599671]
  We propose a new training paradigm for general image restoration models, which we name bfReview Learning. This approach begins with sequential training of an image restoration model on several degraded datasets, combined with a review mechanism. We design a lightweight all-purpose image restoration network that can efficiently reason about degraded images with 4K resolution on a single consumer-grade GPU.
  arXiv  Detail & Related papers  (2024-08-13T08:08:45Z)
• 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)
• Generalizing to Out-of-Sample Degradations via Model Reprogramming [29.56470202794348]
  Out-of-Sample Restoration (OSR) task aims to develop restoration models capable of handling out-of-sample degradations. We propose a model reprogramming framework, which translates out-of-sample degradations by quantum mechanic and wave functions.
  arXiv  Detail & Related papers  (2024-03-09T11:56:26Z)
• Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction [62.955327005837475]
  Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. We present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types.
  arXiv  Detail & Related papers  (2023-10-29T13:58:57Z)
• Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey [96.99328714941657]
  We present a comprehensive review of recent diffusion model-based methods on image restoration. We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR. We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• Super-resolution Reconstruction of Single Image for Latent features [8.857209365343646]
  Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. It is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling.
  arXiv  Detail & Related papers  (2022-11-16T09:37:07Z)
• Invertible Rescaling Network and Its Extensions [118.72015270085535]
  In this work, we propose a novel invertible framework to model the bidirectional degradation and restoration from a new perspective. We develop invertible models to generate valid degraded images and transform the distribution of lost contents. Then restoration is made tractable by applying the inverse transformation on the generated degraded image together with a randomly-drawn latent variable.
  arXiv  Detail & Related papers  (2022-10-09T06:58:58Z)
• Attention Based Real Image Restoration [48.933507352496726]
  Deep convolutional neural networks perform better on images containing synthetic degradations. This paper proposes a novel single-stage blind real image restoration network (R$2$Net)
  arXiv  Detail & Related papers  (2020-04-26T04:21:49Z)

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.