Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration

• URL: http://arxiv.org/abs/2407.03636v1
• Date: Thu, 4 Jul 2024 05:01:10 GMT
• Title: Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration
• Authors: Yuhong Zhang, Hengsheng Zhang, Xinning Chai, Zhengxue Cheng, Rong Xie, Li Song, Wenjun Zhang,
• Abstract summary: 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.
• Score: 19.87693298262894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration is a classic low-level problem aimed at recovering high-quality images from low-quality images with various degradations such as blur, noise, rain, haze, etc. However, due to the inherent complexity and non-uniqueness of degradation in real-world images, it is challenging for a model trained for single tasks to handle real-world restoration problems effectively. Moreover, existing methods often suffer from over-smoothing and lack of realism in the restored results. To address these issues, we propose Diff-Restorer, a universal image restoration method based on the diffusion model, aiming to leverage the prior knowledge of Stable Diffusion to remove degradation while generating high perceptual quality restoration results. Specifically, we utilize the pre-trained visual language model to extract visual prompts from degraded images, including semantic and degradation embeddings. The semantic embeddings serve as content prompts to guide the diffusion model for generation. In contrast, the degradation embeddings modulate the Image-guided Control Module to generate spatial priors for controlling the spatial structure of the diffusion process, ensuring faithfulness to the original image. Additionally, we design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain. We conducted comprehensive qualitative and quantitative analysis on restoration tasks with different degradations, demonstrating the effectiveness and superiority of our approach.

Related papers

• DiffLoss: unleashing diffusion model as constraint for training image restoration network [4.8677910801584385]
  We introduce a new perspective that implicitly leverages the diffusion model to assist the training of image restoration network, called DiffLoss. By combining these two designs, the overall loss function is able to improve the perceptual quality of image restoration, resulting in visually pleasing and semantically enhanced outcomes.
  arXiv  Detail & Related papers  (2024-06-27T09:33:24Z)
• DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution [19.33582308829547]
  This paper proposes to leverage degradation-aligned language prompt for accurate, fine-grained, and high-fidelity image restoration. The proposed method achieves a new state-of-the-art perceptual quality level.
  arXiv  Detail & Related papers  (2024-06-24T09:30:36Z)
• Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models [14.25759541950917]
  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)
  arXiv  Detail & Related papers  (2024-04-15T12:34:21Z)
• DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion [27.52552274944687]
  We introduce a novel two-stage, degradation-aware framework that enhances the diffusion model's ability to recognize content and degradation in low-resolution images. In the first stage, we employ unsupervised contrastive learning to obtain representations of image degradations. In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations.
  arXiv  Detail & Related papers  (2024-03-31T12:07:04Z)
• All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation [47.00239809958627]
  We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
  arXiv  Detail & Related papers  (2023-08-06T04:51:41Z)
• 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)
• 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)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)
• Gated Fusion Network for Degraded Image Super Resolution [78.67168802945069]
  We propose a dual-branch convolutional neural network to extract base features and recovered features separately. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process.
  arXiv  Detail & Related papers  (2020-03-02T13:28:32Z)

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.