ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration

Abstract

Recently, All-in-One image restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches heavily rely on degradation-specific representation learning, which can lead to oversmoothing and artifacts in the restored images. To address this limitation, we propose ClearAIR, a novel AiOIR framework inspired by human visual perception and designed with a hierarchical restoration strategy in a coarse-to-fine manner. First, leveraging the global priority characteristic of early human visual perception, we employ an image quality assessment model to evaluate the overall image structure and degradation level. Next, we introduce a Semantic Guidance Unit to provide coarse semantic region guidance and a Task Identifier to predict local degradation types, enabling a more informed characterization of local degradation patterns. Finally, aiming at the challenge of local detail restoration, we propose an Internal Clue Reuse Mechanism that deeply mines the internal information of the image in a self-supervised manner to enhance the model’s capacity for fine-detail recovery. Experimental results demonstrate that ClearAIR achieves superior restoration performance across diverse synthetic and real-world datasets.