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This project is a deep learning computer vision implementation focused on low-light image restoration. It uses a neural network to process raw sensor data, mapping underexposed images to well-exposed versions to improve visibility and restore natural colors. The implementation is based on CVPR 2018 research and utilizes TensorFlow to execute the computational graph. It employs a convolutional neural network and pixel-wise regression to reconstruct scene lighting directly from unprocessed raw image data. The project includes a framework for supervised pair learning, where models are trained u
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön Department of Information Technology, Uppsala University
1.Sichuan University, 2.Southwest Jiaotong University, 3.University of Electronic Science and Technology of China, 4.Shanghai Jiaotong University, 5.Megvii Technology
The main features of jianghaiscu/lightendiffusion are: Image Restoration, Low Light Enhancement.
Projects with overlapping indexed features include: cchen156/learning-to-see-in-the-dark — This project is a deep learning computer vision implementation focused on low-light image restoration. It uses a… algolzw/daclip-uir — Project Page | Paper | Model Card 🤗. algolzw/image-restoration-sde — Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön Department of Information Technology,… auroral703/pertouch. bahjat-kawar/ddrm — arXiv | PDF | Project Website. aaaasan111/difflid.