labcisne/CT-Super-Resolution
This project focuses on enhancing low-dosage computed tomography (CT) images using deep learning-based super-resolution techniques. The goal is to compare several state-of-the-art models to reconstruct high-quality CT images from noisy, low-dose scans.
This project helps medical professionals enhance the clarity of low-dose CT scans. It takes noisy, low-quality CT images as input and generates high-resolution versions, making it easier to see fine details. Radiologists, imaging specialists, and medical researchers would find this valuable for diagnostic accuracy and study.
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Use this if you need to improve the visual quality and detail of CT scans acquired with lower radiation doses.
Not ideal if you are looking to process other types of medical images or require real-time image enhancement during live procedures.
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Last pushed
Dec 16, 2024
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