niuchuangnn/noise2sim
Noise2Sim is a general unsupervised deep denoising method.
Noise2Sim helps medical imaging specialists, researchers, or anyone working with medical scans to clarify noisy images. It takes raw, low-dose CT or photon-counting CT scan data as input and produces clearer, denoised images, making it easier to analyze for diagnoses or research without needing perfectly clean reference images.
No commits in the last 6 months. Available on PyPI.
Use this if you need to improve the clarity of medical or natural images suffering from significant noise, especially from low-dose CT scans, without access to perfectly noise-free examples.
Not ideal if your primary goal is real-time image processing on standard consumer hardware, as it's geared towards deep learning training and inference on GPU-enabled systems.
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Python
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Last pushed
Jan 06, 2022
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