shermanlian/spatial-entropy-loss
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement, CVPRW 2024. Best LPIPS in NTIRE chanllenge.
This tool helps photographers, videographers, and digital artists improve the quality of images taken in low-light conditions. It takes dark, noisy images and transforms them into clearer, brighter, and more visually appealing ones, enhancing details that might otherwise be lost. It's designed for professionals or hobbyists who work with photography or digital media and need to rescue underexposed shots.
No commits in the last 6 months.
Use this if you need to significantly brighten and clarify images captured in very dim lighting without introducing excessive noise or artifacts.
Not ideal if you're looking for a simple, one-click image editor for minor adjustments, or if you prefer a graphical user interface.
Stars
16
Forks
2
Language
Python
License
MIT
Category
Last pushed
Apr 26, 2024
Commits (30d)
0
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