A ResUNet predicts per-pixel brightness and contrast parameters to automatically normalize radiographic X-ray images, achieving 24.75 dB PSNR and 0.8431 SSIM on a clinical test set.
C., ``Learning to enhance low-light image via zero-reference deep curve estimation,'' IEEE transactions on pattern analysis and machine intelligence 44 (8), 4225--4238 (2021)
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Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping
A ResUNet predicts per-pixel brightness and contrast parameters to automatically normalize radiographic X-ray images, achieving 24.75 dB PSNR and 0.8431 SSIM on a clinical test set.