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Exposure_Correction is a research project that provides the implementation for the paper Learning Multi-Scale Photo Exposure Correction (CVPR 2021). The repository focuses on correcting poorly exposed photographs, handling both underexposure and overexposure using a deep learning approach. The method employs a multi-scale framework that learns to enhance images by adjusting exposure levels across different spatial resolutions. This allows the model to preserve fine details while correcting global lighting inconsistencies. The repository includes pre-trained models, datasets, and training/testing code to enable reproducibility and experimentation. By leveraging this framework, researchers and developers can apply exposure correction to a wide range of natural images, improving visual quality without manual editing. The project serves both as a research reference and a practical tool for computational photography and image enhancement.
Features
- Implementation of multi-scale photo exposure correction (CVPR 2021)
- Handles both underexposed and overexposed images
- Pre-trained models and datasets provided for reproducibility
- Training and testing scripts for experimentation
- Preserves fine details while correcting global exposure
- Applicable to computational photography and vision applications
Project Samples
