D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images
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Abstract (arXiv)
Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient methods have been proposed to enhance LDCT quality, many overestimate noise and perform excessive smoothing, leading to a loss of critical details. In this paper, we introduce D-PerceptCT, a novel architecture inspired by key principles of the Human Visual System (HVS) to enhance LDCT images. The objective is to guide the model to enhance or preserve perceptually relevant features, thereby providing radiologists with CT images where critical anatomical structures and fine pathological details are perceptu- ally visible. D-PerceptCT consists of two main blocks: 1) a Visual Dual-path Extractor (ViDex), which integrates semantic priors from a pretrained DINOv2 model with local spatial features, allowing the network to incorporate semantic-awareness during enhancement; (2) a Global-Local State-Space block that captures long-range information and multiscale features to preserve the important structures and fine details for diagnosis. In addition, we propose a novel deep perceptual loss, designated as the Deep Perceptual Relevancy Loss Function (DPRLF), which is inspired by human contrast sensitivity, to further emphasize perceptually important features. Extensive experiments on the Mayo2016 dataset demonstrate the effectiveness of D-PerceptCT method for LDCT enhancement, showing better preservation of structural and textural information within LDCT images compared to SOTA methods.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 18 Nov 2025.
Builds on
19 of 35 references.
References · 19
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
- 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
- 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 301
- 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
- 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
- 2024DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignmentcited by 79
- 2022Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Networkcited by 57
- 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
- 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
- 2022Shift-tolerant Perceptual Similarity Metriccited by 44
7 more
- 2024ColorVideoVDP: A visual difference predictor for image, video and display distortionscited by 32
- 2023LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurringcited by 12
- 2023A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventionscited by 11
- 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
- 2023Solving Low-Dose CT Reconstruction via GAN with Local Coherencecited by 5
- 2024CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoisingcited by 4
- 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedupcited by 3
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