Learnable Total Variation with Lambda Mapping for Low-Dose CT Denoising
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 13 Nov 2025.
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References · 8
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
- 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
- 2017Learned Primal-dual Reconstructioncited by 251
- 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
- 2023Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrollingcited by 14
- 2024Space-Variant Total Variation boosted by learning techniques in few-view tomographic imagingcited by 2
- 2025Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imagingcited by 2
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