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New ENCORE framework enhances low-dose CT image denoising

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called ENCORE for denoising low-dose computed tomography (CT) images. This framework explicitly considers the unique noise characteristics of CT scans, moving beyond generic image denoising models. ENCORE reformulates noise synthesis based on a more realistic noise distribution and extracts local noise power and correlation contexts. It also introduces a FlyingConv module that adaptively adjusts convolution weights for different image regions, improving both denoising quality and computational efficiency. Additionally, ENCORE allows for zero-shot conditional denoising by manipulating noise context maps at inference time, enabling dynamic control over output image texture. AI

RANK_REASON The cluster contains a research paper detailing a new framework for image denoising. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ENCORE framework enhances low-dose CT image denoising

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The cluster contains a research paper detailing a new framework for image denoising. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Minwoo Yu, N. Robert Bennett, Jongduk Baek, Adam S. Wang ·

    ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising

    arXiv:2608.10343v1 Announce Type: new Abstract: While deep learning-based denoising has become widely adopted in low-dose CT, conventional models use generic architectures designed for natural images, failing to account for non-stationary and spatially correlated CT noise charact…

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