<p>Image exposure correction tasks often involve addressing issues such as overexposure, underexposure, and uneven exposure. These exposure errors can result in loss of image details, color distortion, and significant degradation of image quality. Exposure correction can be divided into two main challenges: recovering structural information like details and colors, and restoring proper illumination levels. To tackle these challenges more effectively, we propose a dual-prompt approach that introduces structural prompts and illumination prompts to guide the network’s learning process. (i) Structural prompts consist of a set of learnable parameters that help the network adopt specific restoration strategies based on different lighting conditions. For varying degrees of overexposed and underexposed inputs, structural prompts guide the network to apply the appropriate structural restoration strategies to better preserve and enhance image details. (ii) Illumination prompts are divided into positive text prompts and negative text prompts. Negative prompts are further categorized into overexposure negative prompts and underexposure negative prompts. We use CLIP (Contrastive Language–Image Pretraining) to align text prompts with images, creating loss functions that accurately assess the exposure state of the image. Compared to directly inputting text descriptions into CLIP, our pre-trained, learnable text prompt parameters more robustly guide the loss function to make precise assessments of the input image’s exposure quality. In summary, our dual-prompt approach effectively addresses the key issues of detail recovery and illumination estimation in exposure correction tasks through the combined action of structural and illumination prompts.</p>