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, despite being designed to handle different types of degradations, extensive experiments demonstrate that our method performs competitively to the task-specific algorithms.

翻译：图像复原旨在从退化观测中恢复高质量图像。由于现有方法多专注于单一退化去除任务，其在其他类型退化上的表现不尽如人意，难以满足真实场景中的多样化应用需求。本文提出一种面向数据成分的新型方法，利用提示学习实现单个模型高效处理多重图像退化任务。具体而言，我们采用编码器捕获特征，并引入包含退化特定信息的提示，引导解码器自适应恢复受不同退化影响的图像。为建模局部不变特性与非局部信息以实现高质量图像复原，我们融合了CNN操作与Transformer。同时，我们对Transformer模块（基于提示的多头重组注意力机制与简单门控前馈网络）进行了多项关键设计，以降低计算需求并选择性保留有助于潜在清晰图像高效恢复的信息。此外，我们引入特征融合机制进一步挖掘多尺度信息以增强聚合特征。最终形成的紧密互联层级架构CAPTNet虽专为处理不同类型退化而设计，但大量实验表明，该方法在性能上与任务特定算法具有竞争力。