RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models

• URL: http://arxiv.org/abs/2407.18035v1
• Date: Thu, 25 Jul 2024 13:29:37 GMT
• Title: RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
• Authors: Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Sixiang Chen, Tian Ye, Renjing Pei, Kaiwen Zhou, Fenglong Song, Lei Zhu,
• Abstract summary: We introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts.
• Score: 45.88103575837924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts. Furthermore, the system modular design facilitates the fast integration of new tasks and models, enhancing its flexibility and scalability for various applications.

Related papers

• Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers [53.298698981438]
  We propose Universal Image Restoration (UIR), a new task setting that requires models to be trained on a set of degradation bases and then remove any degradation that these bases can potentially compose in a zero-shot manner. Inspired by the Chain-of-Thought which prompts LLMs to address problems step-by-step, we propose the Chain-of-Restoration (CoR) CoR instructs models to step-by-step remove unknown composite degradations.
  arXiv  Detail & Related papers  (2024-10-11T10:21:42Z)
• UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation [50.27688690379488]
  Existing unified methods treat multi-degradation image restoration as a multi-task learning problem. We propose a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks.
  arXiv  Detail & Related papers  (2024-09-30T11:16:56Z)
• Training-Free Large Model Priors for Multiple-in-One Image Restoration [24.230376300759573]
  Large Model Driven Image Restoration framework (LMDIR) Our architecture comprises a query-based prompt encoder, degradation-aware transformer block injecting global degradation knowledge. This design facilitates single-stage training paradigm to address various degradations while supporting both automatic and user-guided restoration.
  arXiv  Detail & Related papers  (2024-07-18T05:40:32Z)
• Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance [12.066756224383827]
  textbfRestorer is a novel Transformer-based all-in-one image restoration model. It can handle composite degradation in real-world scenarios without requiring additional training. It is efficient during inference, suggesting the potential in real-world applications.
  arXiv  Detail & Related papers  (2024-06-18T13:18:32Z)
• Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration [58.11518043688793]
  MPerceiver is a novel approach to enhance adaptiveness, generalizability and fidelity for all-in-one image restoration. MPerceiver is trained on 9 tasks for all-in-one IR and outperforms state-of-the-art task-specific methods across most tasks.
  arXiv  Detail & Related papers  (2023-12-05T17:47:11Z)
• Multi-task Image Restoration Guided By Robust DINO Features [88.74005987908443]
  We propose mboxtextbfDINO-IR, a multi-task image restoration approach leveraging robust features extracted from DINOv2. We first propose a pixel-semantic fusion (PSF) module to dynamically fuse DINOV2's shallow features. By formulating these modules into a unified deep model, we propose a DINO perception contrastive loss to constrain the model training.
  arXiv  Detail & Related papers  (2023-12-04T06:59:55Z)
• Prompt-based Ingredient-Oriented All-in-One Image Restoration [0.0]
  We propose a novel data ingredient-oriented approach to tackle multiple image degradation tasks. Specifically, we utilize a encoder to capture features and introduce prompts with degradation-specific information to guide the decoder. Our method performs competitively to the state-of-the-art.
  arXiv  Detail & Related papers  (2023-09-06T15:05:04Z)
• Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement [75.25451566988565]
  We propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.
  arXiv  Detail & Related papers  (2023-04-13T17:54:00Z)

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.