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Learning multi-scale deep model correcting over- and under- exposed
Exposure_Correction is a research project that provides the implementation for the paper Learning Multi-Scale Photo ExposureCorrection (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. ...
Learning infinite-resolution image processing with GAN and RL
...Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product.
dlRaw manipulates raw images from digital cameras. Besides dcraw functions, it does exposurecorrection, curve application in RGB/CIELAB space and USM or Refocus sharpening. Includes lensfun for lens corrections and greycstoration for noise reduction
jdlRaw manipulates raw images from digital cameras and is based on dcraw and imagemagick. It does exposurecorrection, curve application in RGB/CIELAB space and USM/Refocus sharpening. Includes also lensfun and noise reduction algorithms.
flRaw manipulates raw images from digital cameras. Besides usual dcraw settings it can do exposurecorrection, curve application in RGB/CIELAB space and USM sharpening in CIELAB space.