RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction

• URL: http://arxiv.org/abs/2512.07191v1
• Date: Mon, 08 Dec 2025 06:06:29 GMT
• Title: RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction
• Authors: Wenqi Zhao, Jiacheng Sang, Fenghua Cheng, Yonglu Shu, Dong Li, Xiaofeng Yang,
• Abstract summary: We propose a novel variational Reflectance-based Level Set Model (RefLSM) for medical image segmentation.<n>RefLSM explicitly integrates Retinex-inspired reflectance decomposition into the segmentation framework.<n>We show that RefLSM achieves superior segmentation accuracy, robustness, and computational efficiency compared to state-of-the-art level set methods.
• Score: 10.716406019360441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation remains challenging due to intensity inhomogeneity, noise, blurred boundaries, and irregular structures. Traditional level set methods, while effective in certain cases, often depend on approximate bias field estimations and therefore struggle under severe non-uniform imaging conditions. To address these limitations, we propose a novel variational Reflectance-based Level Set Model (RefLSM), which explicitly integrates Retinex-inspired reflectance decomposition into the segmentation framework. By decomposing the observed image into reflectance and bias field components, RefLSM directly segments the reflectance, which is invariant to illumination and preserves fine structural details. Building on this foundation, we introduce two key innovations for enhanced precision and robustness. First, a linear structural prior steers the smoothed reflectance gradients toward a data-driven reference, providing reliable geometric guidance in noisy or low-contrast scenes. Second, a relaxed binary level-set is embedded in RefLSM and enforced via convex relaxation and sign projection, yielding stable evolution and avoiding reinitialization-induced diffusion. The resulting variational problem is solved efficiently using an ADMM-based optimization scheme. Extensive experiments on multiple medical imaging datasets demonstrate that RefLSM achieves superior segmentation accuracy, robustness, and computational efficiency compared to state-of-the-art level set methods.

Related papers

• MirrorLA: Reflecting Feature Map for Vision Linear Attention [49.41670925034762]
  Linear attention significantly reduces the computational complexity of Transformers from quadratic to linear, yet it consistently lags behind softmax-based attention in performance.<n>We propose MirrorLA, a geometric framework that substitutes passive truncation with active reorientation.<n>MirrorLA achieves state-of-the-art performance across standard benchmarks, demonstrating that strictly linear efficiency can be achieved without compromising representational fidelity.
  arXiv  Detail & Related papers  (2026-02-04T09:14:09Z)
• PDE-Constrained Optimization for Neural Image Segmentation with Physics Priors [0.0]
  Images of microscopy images constitutes an ill-posed inverse problem due to measurement noise, weak object boundaries, and limited labeled data.<n>In this work, image segmentation is formulated as a PDE-constrained optimization problem that integrates physically motivated priors into deep learning models.<n>Experiments are conducted on the LIVECell dataset, a high-quality, manually annotated collection of phase-contrast microscopy images.
  arXiv  Detail & Related papers  (2026-02-01T07:28:14Z)
• Zero-Reference Joint Low-Light Enhancement and Deblurring via Visual Autoregressive Modeling with VLM-Derived Modulation [18.67176370944511]
  Real-world dark images commonly exhibit not only low visibility and contrast but also complex noise and blur, posing significant restoration challenges.<n>We propose a generative framework based on visual autoregressive ( VAR) modeling, guided by perceptual priors from the vision-language model (VLM)<n>Our framework is fully unsupervised and achieves state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2025-11-23T19:08:45Z)
• Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction [5.479463752172751]
  Diffusion models have demonstrated remarkable generative capabilities in image processing tasks.<n>We propose a Sparse condition Temporal Rewighted Integrated Distribution Estimation guided diffusion model (STRIDE) for sparse-view CT reconstruction.<n> Experimental results on both public and real datasets demonstrate that the proposed method achieves the best improvement of 2.58 dB in PSNR, increase of 2.37% in SSIM, and reduction of 0.236 in MSE.
  arXiv  Detail & Related papers  (2025-09-07T09:42:16Z)
• Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement [1.8724535169356553]
  Event cameras provide performance gain for low-light image enhancement.<n>Currently, existing event-based methods feed a frame and events directly into a single model.<n>We propose a visibility restoration network with amplitude-phase entanglement.<n>In the second stage, a fusion strategy with dynamic alignment is proposed to mitigate the spatial mismatch.
  arXiv  Detail & Related papers  (2025-08-01T04:25:00Z)
• Towards Generalized Range-View LiDAR Segmentation in Adverse Weather [65.22588361803942]
  We identify and analyze the unique challenges that affect the generalization of range-view LiDAR segmentation in severe weather.<n>We propose a modular and lightweight framework that enhances robustness without altering the core architecture of existing models.<n>Our approach significantly improves generalization to adverse weather with minimal inference overhead.
  arXiv  Detail & Related papers  (2025-06-10T16:48:27Z)
• Dereflection Any Image with Diffusion Priors and Diversified Data [86.15504914121226]
  We propose a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal.<n>First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes.<n>Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference.
  arXiv  Detail & Related papers  (2025-03-21T17:48:14Z)
• InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration [106.70903819362402]
  Diffusion priors have been used for blind face restoration (BFR) by fine-tuning diffusion models (DMs) on restoration datasets to recover low-quality images.<n>We propose InterLCM to leverage the latent consistency model (LCM) for its superior semantic consistency and efficiency.<n>InterLCM outperforms existing approaches in both synthetic and real-world datasets while also achieving faster inference speed.
  arXiv  Detail & Related papers  (2025-02-04T10:51:20Z)
• Guided MRI Reconstruction via Schrödinger Bridge [15.448360566887407]
  Cross-contrast priors can be exploited to improve image reconstruction from undersampled data.<n>$mathbfI2$SB-Inversion is a guided reconstruction framework based on the Schr"odinger Bridge (SB)
  arXiv  Detail & Related papers  (2024-11-21T16:25:56Z)
• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging [17.511583657111792]
  Snapshot spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. We introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to deep unfolding method.
  arXiv  Detail & Related papers  (2023-11-24T04:55:20Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.