Image and Video Processing Papers
@CIGX
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Theory, algorithms, and architectures for images, video, and multidimensional signals submissions to https://t.co/b5iXv7PYp3 (not affiliated with arXiv)
Joined November 2010
Prompt-based Multimodal Semantic Communication for Multi-spectral Image Segmentation.
arxiv.org
Multimodal semantic communication has gained widespread attention due to its ability to enhance downstream task performance. A key challenge in such systems is the effective fusion of features...
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Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation.
arxiv.org
Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In this work, we...
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A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications.
arxiv.org
Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across...
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Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing.
arxiv.org
Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded...
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Deep Learning Architectures for Medical Image Denoising: A Comparative Study of CNN-DAE, CADTra, and DCMIEDNet.
arxiv.org
Medical imaging modalities are inherently susceptible to noise contamination that degrades diagnostic utility and clinical assessment accuracy. This paper presents a comprehensive comparative...
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Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network.
arxiv.org
Contrast-enhanced computed tomography (CT) imaging is essential for diagnosing and monitoring thoracic diseases, including aortic pathologies. However, contrast agents pose risks such as...
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Multimodal Medical Endoscopic Image Analysis via Progressive Disentangle-aware Contrastive Learning.
arxiv.org
Accurate segmentation of laryngo-pharyngeal tumors is crucial for precise diagnosis and effective treatment planning. However, traditional single-modality imaging methods often fall short of...
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Analysis of Transferability Estimation Metrics for Surgical Phase Recognition.
arxiv.org
Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data. In surgical video analysis, where...
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Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence.
arxiv.org
Brain tumour imaging assessment typically requires both pre- and post-contrast MRI, but gadolinium administration is not always desirable, such as in frequent follow-up, renal impairment, allergy,...
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Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing.
arxiv.org
Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order...
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MambaIC: State Space Models for High-Performance Learned Image Compression.
arxiv.org
A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational inefficiency and...
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RedDino: A foundation model for red blood cell analysis.
arxiv.org
Red blood cells (RBCs) are essential to human health, and their precise morphological analysis is important for diagnosing hematological disorders. Despite the promise of foundation models in...
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Evaluating the Predictive Value of Preoperative MRI for Erectile Dysfunction Following Radical Prostatectomy.
arxiv.org
Accurate preoperative prediction of erectile dysfunction (ED) is important for counseling patients undergoing radical prostatectomy. While clinical features are established predictors, the added...
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Evaluation of 3D Counterfactual Brain MRI Generation.
arxiv.org
Counterfactual generation offers a principled framework for simulating hypothetical changes in medical imaging, with potential applications in understanding disease mechanisms and generating...
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Improving U-Net Confidence on TEM Image Data with L2-Regularization, Transfer Learning, and Deep Fine-Tuning.
arxiv.org
With ever-increasing data volumes, it is essential to develop automated approaches for identifying nanoscale defects in transmission electron microscopy (TEM) images. However, compared to features...
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Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction.
arxiv.org
The computational imaging technique of Fourier Ptychographic Microscopy (FPM) enables high-resolution imaging with a wide field of view and can serve as an extremely valuable tool, e.g. in the...
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A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer.
arxiv.org
The non-invasive assessment of increasingly incidentally discovered renal masses is a critical challenge in urologic oncology, where diagnostic uncertainty frequently leads to the overtreatment of...
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Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution.
arxiv.org
Diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrated impressive performance. To achieve efficient Real-ISR, many works employ Variational Score Distillation (VSD)...
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Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization.
arxiv.org
Histopathology remains the gold standard for cancer diagnosis and prognosis. With the advent of transcriptome profiling, multi-modal learning combining transcriptomics with histology offers more...
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Decoding MGMT Methylation: A Step Towards Precision Medicine in Glioblastoma.
arxiv.org
Glioblastomas, constituting over 50% of malignant brain tumors, are highly aggressive brain tumors that pose substantial treatment challenges due to their rapid progression and resistance to...
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