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.
@article{arxiv.2511.10500,
title = {Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising},
author = {Yusuf Talha Basak and Mehmet Ozan Unal and Metin Ertas and Isa Yildirim},
journal= {arXiv preprint arXiv:2511.10500},
year = {2026}
}