Guidance Network with Staged Learning for Image enhancement - Liang, Luming; Zharkov, Illya; Amjadi, Faezeh; Vaezi Joze, Hamid; Pradeep, Vivek (The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021)
Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.