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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s10278-013-9622-7openalex-snapshot
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
inferredcites →https://doi.org/10.1007/s11042-022-12406-0openalex-snapshot
CNN-based denoising system for the image quality enhancement
inferredcites →https://doi.org/10.1016/j.ins.2022.07.122openalex-snapshot
True wide convolutional neural network for image denoising
inferredcites →https://doi.org/10.1056/nejmoa1911793openalex-snapshot
Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial
inferredcites →https://doi.org/10.1109/83.841940openalex-snapshot
Image quality assessment based on a degradation model
inferredcites →https://doi.org/10.1109/access.2019.2943319openalex-snapshot
A Comprehensive Performance Evaluation of Image Quality Assessment Algorithms
inferredcites →https://doi.org/10.1109/access.2021.3061062openalex-snapshot
A Residual Dense U-Net Neural Network for Image Denoising
inferredcites →https://doi.org/10.1109/access.2023.3335372openalex-snapshot
LPMsDE: Multi-Scale Denoising and Enhancement Method Based on Laplacian Pyramid Framework for Forward-Looking Sonar Image
inferredcites →https://doi.org/10.1109/access.2024.3383028openalex-snapshot
No Reference Image Quality Assessment Based on DCT and SOM Clustering
inferredcites →https://doi.org/10.1109/acssc.2003.1292216openalex-snapshot
Multiscale structural similarity for image quality assessment
inferredcites →https://doi.org/10.1109/cso.2012.88openalex-snapshot
Image Change Detection Based on the Minimum Mean Square Error
inferredcites →https://doi.org/10.1109/embc40787.2023.10340822openalex-snapshot
Implementing Natural Image Quality Evaluator for Performance Indicator on Noise Artefacts Recovery in CT Scan
inferredcites →https://doi.org/10.1109/iccic.2015.7435737openalex-snapshot
Comparative analysis of noise removal techniques in MRI brain images
inferredcites →https://doi.org/10.1109/iccict50803.2021.9510078openalex-snapshot
Chest X-ray Segmentation Using Watershed and Super Pixel Segmentation Technique
inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot
Learning Deconvolution Network for Semantic Segmentation
inferredcites →https://doi.org/10.1109/icicv62344.2024.00149openalex-snapshot
Development of Advanced Noise Filtering Techniques for Medical Image Enhancement
inferredcites →https://doi.org/10.1109/icsgrc55096.2022.9845152openalex-snapshot
An Image Denoising Model using Deep Learning for Digital Breast Tomosynthesis Images
inferredcites →https://doi.org/10.1109/isivc.2016.7894006openalex-snapshot
A study on the usability of opinion-unaware no-reference natural image quality metrics in the context of medical images
inferredcites →https://doi.org/10.1109/jstars.2024.3422175openalex-snapshot
A Noise-Robust Blind Deblurring Algorithm With Wavelet-Enhanced Diffusion Model for Optical Remote Sensing Images
inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-snapshot
Making a “Completely Blind” Image Quality Analyzer
inferredcites →https://doi.org/10.1109/lsp.2013.2244081openalex-snapshot
Edge Strength Similarity for Image Quality Assessment
inferredcites →https://doi.org/10.1109/ncc.2015.7084843openalex-snapshot
Blind image quality evaluation using perception based features
inferredcites →https://doi.org/10.1109/ncc.2019.8732256openalex-snapshot
Edge Preserved Herringbone Artifact Removal from MRI Using Two-Stage Variational Mode Decomposition
inferredcites →https://doi.org/10.1109/qomex.2012.6263880openalex-snapshot
Peak signal-to-noise ratio revisited: Is simple beautiful?
inferredcites →https://doi.org/10.1109/spcom50965.2020.9179556openalex-snapshot
A ‘Complete Blind’ No-Reference Stereoscopic Image Quality Assessment Algorithm
inferredcites →https://doi.org/10.1109/tbme.2015.2476371openalex-snapshot
Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter
inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-snapshot
Image quality assessment: from error visibility to structural similarity
inferredcites →https://doi.org/10.1109/tip.2007.901238openalex-snapshot
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
inferredcites →https://doi.org/10.1109/tip.2012.2214050openalex-snapshot
No-Reference Image Quality Assessment in the Spatial Domain
inferredcites →https://doi.org/10.1109/tip.2015.2426416openalex-snapshot
A Feature-Enriched Completely Blind Image Quality Evaluator
inferredcites →https://doi.org/10.1109/tip.2018.2839891openalex-snapshot
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
inferredcites →https://doi.org/10.1109/tmi.2008.918322openalex-snapshot
Fast Noise Reduction in Computed Tomography for Improved 3-D Visualization
inferredcites →https://doi.org/10.1109/tmi.2017.2715284openalex-snapshot
Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
inferredcites →https://doi.org/10.1109/tmm.2020.3033117openalex-snapshot
Predicting the Perceptual Quality of Point Cloud: A 3D-to-2D Projection-Based Exploration
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1186/s42492-019-0016-7openalex-snapshot
Brief review of image denoising techniques
inferredcites →https://doi.org/10.1200/jco.2012.43.3110openalex-snapshot
Computed Tomography Screening for Lung Cancer: Has It Finally Arrived? Implications of the National Lung Screening Trial
inferredcites →https://doi.org/10.3390/app12041986openalex-snapshot
Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images
inferredcites →https://doi.org/10.3390/electronics13132640openalex-snapshot
Enhancing Medical Image Denoising: A Hybrid Approach Incorporating Adaptive Kalman Filter and Non-Local Means with Latin Square Optimization
inferredcites →https://doi.org/10.3390/s22187031openalex-snapshot
Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net
inferredcites →https://doi.org/10.3390/s23073440openalex-snapshot
Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm
inferredcites →https://doi.org/10.3390/signals3030028openalex-snapshot
Saliency-Guided Local Full-Reference Image Quality Assessment
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1109/ic3it66137.2025.11340892openalex-snapshot
Medical Image Fusion of Magnetic Resonance Imaging and Computed Tomography Using Learned Wavelet Complex Adapter
inferred← citeshttps://doi.org/10.17946/jrst.2025.48.5.545openalex-snapshot
Low-Dose Abdominal CT Image Noise Reduction Using Residual-Based Median Adaptive Gaussian Filtering Algorithms: Performance Evaluation and Optimization
inferred← citeshttps://doi.org/10.3390/math14142582openalex-snapshot
ResGASP-GAN: A Residual Group-Normalized ASPP-SE GAN with a PatchGAN Discriminator for Low-Light Image Enhancement
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