Prompt-based Ingredient-Oriented All-in-One Image Restoration
Hu Gao, Depeng Dang

TL;DR
This paper introduces CAPTNet, a prompt-based, multi-degradation image restoration model that combines CNNs and Transformers to efficiently recover high-quality images from various degraded inputs.
Contribution
The paper presents a novel ingredient-oriented, prompt-based approach with a hybrid CNN-Transformer architecture for multi-degradation image restoration.
Findings
Performs competitively with state-of-the-art methods
Effectively handles multiple degradation types with a single model
Reduces computational requirements through key Transformer design modifications
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Image Fusion Techniques · Image and Signal Denoising Methods
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Residual Connection · Byte Pair Encoding · Label Smoothing · Dropout · Absolute Position Encodings · Layer Normalization · Adam
