AIMC Topic: Image Enhancement
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General retinal image enhancement via reconstruction: Bridging distribution shifts using latent diffusion adaptors.
Medical image analysis
Apr 26, 2025
Deep learning-based fundus image enhancement has attracted extensive research attention recently, which has shown remarkable effectiveness in improving the visibility of low-quality images. However, these methods are often constrained to specific dat...
Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases.
Scientific reports
Apr 12, 2025
In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive experiments were conducted on a dataset of 759 images di...
Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.
Neuroradiology
Mar 17, 2025
PURPOSE: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learnin...
Deep learning based image enhancement for dynamic non-Cartesian MRI: Application to "silent" fMRI.
Computers in biology and medicine
Mar 3, 2025
Radial based non-Cartesian sequences may be used for silent functional MRI examinations particularly in settings where scanner noise could pose issues. However, to achieve reasonable temporal resolution, under-sampled 3D radial k-space commonly resul...
Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.
Academic radiology
Feb 28, 2025
RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spinal MRI sequences using the denoising diffusion probabilistic model (DDPM) and to compare its performance against one baseline model pix2pix and three a...
Male
Middle Aged
Contrast Media
Magnetic Resonance Imaging
Aged
Adult
Sensitivity and Specificity
Spinal Neoplasms
Image Enhancement
Signal-To-Noise Ratio
Diffusion Magnetic Resonance Imaging
Models, Statistical
Humans
Female
Image Interpretation, Computer-Assisted
Algorithms
Reproducibility of Results
Artifacts
Computer Simulation
A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.
Ultrasound in medicine & biology
Feb 14, 2025
OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.
ILR-Net: Low-light image enhancement network based on the combination of iterative learning mechanism and Retinex theory.
PloS one
Feb 13, 2025
Images captured in nighttime or low-light environments are often affected by external factors such as noise and lighting. Aiming at the existing image enhancement algorithms tend to overly focus on increasing brightness, while neglecting the enhancem...
Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.
Endoscopy
Jan 30, 2025
BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. However, their performance may be different when applied in clinical practice, partly due to the div...
When low-light meets flares: Towards Synchronous Flare Removal and Brightness Enhancement.
Neural networks : the official journal of the International Neural Network Society
Jan 17, 2025
Low-light image enhancement (LLIE) aims to improve the visibility and illumination of low-light images. However, real-world low-light images are usually accompanied with flares caused by light sources, which make it difficult to discern the content o...
Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction.
Academic radiology
Jan 9, 2025
RATIONALE AND OBJECTIVES: To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare ...
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