Efficient Degradation-aware Any Image Restoration
- URL: http://arxiv.org/abs/2405.15475v2
- Date: Sat, 1 Jun 2024 14:39:30 GMT
- Title: Efficient Degradation-aware Any Image Restoration
- Authors: Eduard Zamfir, Zongwei Wu, Nancy Mehta, Danda Pani Paudel, Yulun Zhang, Radu Timofte,
- Abstract summary: We propose textitDaAIR, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime.
By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning.
- Score: 83.92870105933679
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reconstructing missing details from degraded low-quality inputs poses a significant challenge. Recent progress in image restoration has demonstrated the efficacy of learning large models capable of addressing various degradations simultaneously. Nonetheless, these approaches introduce considerable computational overhead and complex learning paradigms, limiting their practical utility. In response, we propose \textit{DaAIR}, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime to collaboratively mine shared aspects and subtle nuances across diverse degradations, generating a degradation-aware embedding. By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning within a unified model. Furthermore, DaAIR introduces a cost-efficient parameter update mechanism that enhances degradation awareness while maintaining computational efficiency. Extensive comparisons across five image degradations demonstrate that our DaAIR outperforms both state-of-the-art All-in-One models and degradation-specific counterparts, affirming our efficacy and practicality. The source will be publicly made available at https://eduardzamfir.github.io/daair/
Related papers
- Efficient Transformer for High Resolution Image Motion Deblurring [0.0]
This paper presents a comprehensive study and improvement of the Restormer architecture for high-resolution image motion deblurring.
We introduce architectural modifications that reduce model complexity by 18.4% while maintaining or improving performance through optimized attention mechanisms.
Our results suggest that thoughtful architectural simplification combined with enhanced training strategies can yield more efficient yet equally capable models for motion deblurring tasks.
arXiv Detail & Related papers (2025-01-30T14:58:33Z) - UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity [79.90839080916913]
We present our UniRestorer with improved restoration performance.
Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model.
In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradationspecific restoration.
arXiv Detail & Related papers (2024-12-28T14:09:08Z) - Numerical Pruning for Efficient Autoregressive Models [87.56342118369123]
This paper focuses on compressing decoder-only transformer-based autoregressive models through structural weight pruning.
Specifically, we propose a training-free pruning method that calculates a numerical score with Newton's method for the Attention and modules, respectively.
To verify the effectiveness of our method, we provide both theoretical support and extensive experiments.
arXiv Detail & Related papers (2024-12-17T01:09:23Z) - Boosting Alignment for Post-Unlearning Text-to-Image Generative Models [55.82190434534429]
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.
This often inadvertently leads to the generation of harmful or inappropriate content and raises copyright concerns.
We propose a framework that seeks an optimal model update at each unlearning iteration, ensuring monotonic improvement on both objectives.
arXiv Detail & Related papers (2024-12-09T21:36:10Z) - Distillation of Diffusion Features for Semantic Correspondence [23.54555663670558]
We propose a novel knowledge distillation technique to overcome the problem of reduced efficiency.
We show how to use two large vision foundation models and distill the capabilities of these complementary models into one smaller model that maintains high accuracy at reduced computational cost.
Our empirical results demonstrate that our distilled model with 3D data augmentation achieves performance superior to current state-of-the-art methods while significantly reducing computational load and enhancing practicality for real-world applications, such as semantic video correspondence.
arXiv Detail & Related papers (2024-12-04T17:55:33Z) - Hierarchical Information Flow for Generalized Efficient Image Restoration [108.83750852785582]
We propose a hierarchical information flow mechanism for image restoration, dubbed Hi-IR.
Hi-IR constructs a hierarchical information tree representing the degraded image across three levels.
In seven common image restoration tasks, Hi-IR achieves its effectiveness and generalizability.
arXiv Detail & Related papers (2024-11-27T18:30:08Z) - 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) - 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) - 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) - Rich Feature Distillation with Feature Affinity Module for Efficient
Image Dehazing [1.1470070927586016]
This work introduces a simple, lightweight, and efficient framework for single-image haze removal.
We exploit rich "dark-knowledge" information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation.
Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains.
arXiv Detail & Related papers (2022-07-13T18:32:44Z)
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.