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In this work, we propose Noise2Quality (N2Q)---a novel self-supervised, non-reference, pixel-wise image quality assessment model to predict IQA maps from LDCTs. Self-supervised dose level prediction as an auxiliary task further improves the model performance.

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In this paper, we propose Noise2Quality (N2Q), a novel NR-IQA method which leverages self-supervised learning to predict structural similarity index map (SSIM) from LDCT images. Our contributions are as follows: • A novel deep learning approach for local IQA through image quality map prediction, as opposed to only

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76K Followers, 433 Following, 2 Posts - ‎᷂ال ᷂نـَـوآل窱.⠀ (@n2q.i)‎ on Instagram: "‎‏⠀ ﴿ ᷂فَسَيَكْفِيكَهُمُ ᷂اللَّهُ ۚ ﴾ ‏⠀‏⠀‏⠀‏⠀‏⠀‏⠀ ‏ ‏ ༯ ᷂احب ᷂الورد⠀‏‎"

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In this work, we propose Noise2Quality (N2Q)---a novel self-supervised, non-reference, pixel-wise image quality assessment model to predict IQA maps from LDCTs. Self-supervised dose level prediction as an auxiliary task further improves the model performance.