INTRINSIC IMAGE DECOMPOSITION BASED ON QUANTIZED PRIOR CODEBOOK

Intrinsic image decomposition is a low-level image processing task that extracts the reflectance and lighting components from an image. This process can improve the illumination robustness of perception tasks, such as object detection, recognition, and image understanding. Recently, deep image generation frameworks have been used to generate intrinsic images. However, the encoder and decoder lack prior knowledge constraints. This paper presents a quantized codebook for embedding intrinsic features that guide the extraction of intrinsic images. To enhance reconstruction accuracy, we propose a purification method to eliminate irrelevant elements from the codebook. Additionally, we propose self-attention and cross-attention modules to integrate the intrinsic features of the codebook into the input image features for reconstruction. The effectiveness of the algorithm is demonstrated through experiments conducted on several popular datasets.