Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918316305547264 |
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| author | Basak, Yusuf Talha Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa |
| author_facet | Basak, Yusuf Talha Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa |
| contents | While Total Variation (TV) excels in noise reduction and edge preservation, its reliance on a scalar regularization parameter limits adaptivity. In this study, we present a Learnable Total Variation (LTV) framework coupling an unrolled TV solver with a LambdaNet that predicts a per-pixel regularization map. The proposed framework is trained end-to-end to optimize reconstruction and regularization jointly, yielding spatially adaptive smoothing. Experiments on the DeepLesion dataset, using realistic LoDoPaB-CT simulation, show consistent gains over classical TV and FBP+U-Net, achieving up to +3.7 dB PSNR and 8% relative SSIM improvement. LTV provides an interpretable alternative to black-box CNNs for low-dose CT denoising. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising Basak, Yusuf Talha Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa Computer Vision and Pattern Recognition While Total Variation (TV) excels in noise reduction and edge preservation, its reliance on a scalar regularization parameter limits adaptivity. In this study, we present a Learnable Total Variation (LTV) framework coupling an unrolled TV solver with a LambdaNet that predicts a per-pixel regularization map. The proposed framework is trained end-to-end to optimize reconstruction and regularization jointly, yielding spatially adaptive smoothing. Experiments on the DeepLesion dataset, using realistic LoDoPaB-CT simulation, show consistent gains over classical TV and FBP+U-Net, achieving up to +3.7 dB PSNR and 8% relative SSIM improvement. LTV provides an interpretable alternative to black-box CNNs for low-dose CT denoising. |
| title | Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.10500 |