Recently, All-in-One image restoration (AIR) techniques have 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 AIR 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, to locate the local degradation areas, we introduce an attention-driven regional analysis process. By combining the Segment Anything Model, we achieve regional semantic positioning and use a task recognizer to identify the degradation patterns of the regions, thus realizing a detailed analysis of the local degradation situations. Finally, aiming at the challenge of local detail restoration, we propose an internal clue reuse mechanism. This mechanism deeply mines the internal information of the image in a self-supervised manner and enhances the model’s learning ability for local details. Experimental results demonstrate that ClearAIR achieves superior restoration results across diverse synthetic and real-world datasets.