Comprehensible Color Filter

Comprehensible color filter research aims to develop methods for accurate and efficient color manipulation in images and videos, addressing challenges like cross-modality inconsistencies and high-resolution processing. Current approaches leverage deep learning, employing architectures that separate color and structural information processing, often within frequency domains or through distinct neural filters for hue, saturation, and value adjustments. This work is significant for improving image and video quality, enabling advancements in applications such as person re-identification, image harmonization, and compression, while also enhancing the interpretability and user control of these complex algorithms.