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.

翻译：图像恢复旨在从退化观测中恢复出高质量图像。由于现有方法大多专注于单一退化的处理，它们在面对其他类型的退化时可能无法获得最优结果，难以满足真实世界场景中的应用需求。本文提出一种新颖的数据成分导向方法，利用基于提示的学习机制，使单一模型能够高效处理多种图像退化任务。具体而言，我们采用编码器提取特征，并引入携带退化特定信息的提示，指导解码器自适应地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恢复，我们结合了卷积神经网络操作与Transformer。同时，我们在Transformer模块（含提示的多头重排注意力机制与简单门控前馈网络）中进行了多项关键设计，以降低计算需求并选择性决定应保留哪些信息，从而促进潜在清晰图像的高效恢复。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。由此形成的紧密交织层级架构被称为CAPTNet，大量实验表明，我们的方法在性能上与现有最先进技术具有竞争力。