Prompt-based Ingredient-Oriented All-in-One Image Restoration
- URL: http://arxiv.org/abs/2309.03063v2
- Date: Tue, 10 Oct 2023 08:30:08 GMT
- Title: Prompt-based Ingredient-Oriented All-in-One Image Restoration
- Authors: Hu Gao and Depeng Dang
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image restoration aims to recover the high-quality images from their degraded
observations. Since most existing methods have been dedicated into single
degradation removal, they may not yield optimal results on other types of
degradations, which do not satisfy the applications in real world scenarios. In
this paper, we propose a novel data ingredient-oriented approach that leverages
prompt-based learning to enable a single model to efficiently tackle multiple
image degradation tasks. Specifically, we utilize a encoder to capture features
and introduce prompts with degradation-specific information to guide the
decoder in adaptively recovering images affected by various degradations. In
order to model the local invariant properties and non-local information for
high-quality image restoration, we combined CNNs operations and Transformers.
Simultaneously, we made several key designs in the Transformer blocks
(multi-head rearranged attention with prompts and simple-gate feed-forward
network) to reduce computational requirements and selectively determines what
information should be persevered to facilitate efficient recovery of
potentially sharp images. Furthermore, we incorporate a feature fusion
mechanism further explores the multi-scale information to improve the
aggregated features. The resulting tightly interlinked hierarchy architecture,
named as CAPTNet, extensive experiments demonstrate that our method performs
competitively to the state-of-the-art.
Related papers
- 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) - Multi-Scale Representation Learning for Image Restoration with State-Space Model [13.622411683295686]
We propose a novel Multi-Scale State-Space Model-based (MS-Mamba) for efficient image restoration.
Our proposed method achieves new state-of-the-art performance while maintaining low computational complexity.
arXiv Detail & Related papers (2024-08-19T16:42:58Z) - 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) - A Mountain-Shaped Single-Stage Network for Accurate Image Restoration [9.431709365739462]
In image restoration, it is typically necessary to maintain a complex balance between spatial details and contextual information.
We propose a single-stage design base on a simple U-Net architecture, which removes or replaces unnecessary nonlinear activation functions.
Our approach, named as M3SNet, outperforms previous state-of-the-art models while using less than half the computational costs.
arXiv Detail & Related papers (2023-05-09T03:18:35Z) - Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network.
We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
arXiv Detail & Related papers (2022-04-19T17:59:45Z) - Multi-Stage Progressive Image Restoration [167.6852235432918]
We propose a novel synergistic design that can optimally balance these competing goals.
Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs.
The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets.
arXiv Detail & Related papers (2021-02-04T18:57:07Z) - 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) - Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task.
We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network.
Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
arXiv Detail & Related papers (2020-03-15T11:04:30Z)
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.