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Shift-Insensitive Perceptual Feature Based on Quadratic Sum of Gradient Magnitude and Log Signals

https://doi.org/10.2139/ssrn.4705652
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A Full-Reference Image Quality Assessment Model Based on Quadratic Gradient Magnitude and LOG Signal
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What Does CNN Shift Invariance Look Like? A Visualization Study
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Visualizing and Understanding Convolutional Networks
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Image registration methods: a survey
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Image Completion Based on Gans with a New Loss Function
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Theory of edge detection
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Multiscale structural similarity for image quality assessment
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Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers
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A convolutional neural network cascade for face detection
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Image Style Transfer Using Convolutional Neural Networks
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Deep Residual Learning for Image Recognition
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Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/have.2019.8921188openalex-extracted
GMSD-based Perceptually Motivated Non-local Means Filter for Image Denoising
inferredcites →https://doi.org/10.1109/icip.2006.313136openalex-extracted
Spatial Pooling Strategies for Perceptual Image Quality Assessment
inferredcites →https://doi.org/10.1109/icip.2007.4379119openalex-extracted
Does where you Gaze on an Image Affect your Perception of Quality? Applying Visual Attention to Image Quality Metric
inferredcites →https://doi.org/10.1109/icip.2010.5649275openalex-extracted
RFSIM: A feature based image quality assessment metric using Riesz transforms
inferredcites →https://doi.org/10.1109/icip.2013.6738831openalex-extracted
Fast image scanning with deep max-pooling convolutional neural networks
inferredcites →https://doi.org/10.1109/jproc.2021.3058954openalex-extracted
Toward Causal Representation Learning
inferredcites →https://doi.org/10.1109/jstsp.2009.2015374openalex-extracted
Visual Importance Pooling for Image Quality Assessment
inferredcites →https://doi.org/10.1109/lsp.2011.2127473openalex-extracted
Non-Shift Edge Based Ratio (NSER): An Image Quality Assessment Metric Based on Early Vision Features
inferredcites →https://doi.org/10.1109/tci.2016.2644865openalex-extracted
Loss Functions for Image Restoration With Neural Networks
inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-extracted
Image quality assessment: from error visibility to structural similarity
inferredcites →https://doi.org/10.1109/tip.2005.859378openalex-extracted
Image information and visual quality
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An information fidelity criterion for image quality assessment using natural scene statistics
inferredcites →https://doi.org/10.1109/tip.2009.2025923openalex-extracted
Complex Wavelet Structural Similarity: A New Image Similarity Index
inferredcites →https://doi.org/10.1109/tip.2010.2092435openalex-extracted
Information Content Weighting for Perceptual Image Quality Assessment
inferredcites →https://doi.org/10.1109/tip.2011.2109730openalex-extracted
FSIM: A Feature Similarity Index for Image Quality Assessment
inferredcites →https://doi.org/10.1109/tip.2012.2219551openalex-extracted
Video Quality Pooling Adaptive to Perceptual Distortion Severity
inferredcites →https://doi.org/10.1109/tip.2013.2293423openalex-extracted
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
inferredcites →https://doi.org/10.1109/tip.2014.2355716openalex-extracted
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
inferredcites →https://doi.org/10.1109/tmi.2016.2527619openalex-extracted
Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development
inferredcites →https://doi.org/10.1109/tmm.2016.2609419openalex-extracted
Maximum a Posterior and Perceptually Motivated Reconstruction Algorithm: A Generic Framework
inferredcites →https://doi.org/10.1109/tpami.1986.4767769openalex-extracted
On Edge Detection
inferredcites →https://doi.org/10.1109/tpami.2008.182openalex-extracted
A Statistical Approach to Material Classification Using Image Patch Exemplars
inferredcites →https://doi.org/10.1109/tuffc.2022.3162800openalex-extracted
Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation
inferredcites →https://doi.org/10.1117/1.3267105openalex-extracted
Most apparent distortion: full-reference image quality assessment and the role of strategy
inferredcites →https://doi.org/10.1117/12.2008446openalex-extracted
Edge patterns extracted from natural images and their statistics for reduced-reference image quality assessment
inferredcites →https://doi.org/10.1117/12.2038982openalex-extracted
LoG acts as a good feature in the task of image quality assessment
inferredcites →https://doi.org/10.1145/3065386openalex-extracted
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3130800.3130816openalex-extracted
HDR image reconstruction from a single exposure using deep CNNs
inferredcites →https://doi.org/10.1145/3357254.3357255openalex-extracted
A convolutional neural network based method for low-illumination image enhancement
inferredcites →https://doi.org/10.1146/annurev.neuro.24.1.1193openalex-extracted
Natural Image Statistics and Neural Representation
inferredcites →https://doi.org/10.1162/neco_a_00163openalex-extracted
A Differential Model of the Complex Cell
inferredcites →https://doi.org/10.2352/issn.2470-1173.2016.18.dpmi-257openalex-extracted
A Reduced-Reference Image Quality Assessment Model Based on Joint-Distribution of Neighboring LOG Signals
inferredcites →https://doi.org/10.2352/j.imagingsci.technol.2010.54.3.030503openalex-extracted
Full Reference Image Quality Assessment Based on Saliency Map Analysis
inferredcites →https://doi.org/10.4018/978-1-5225-7368-5.ch004openalex-extracted
Artificial Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-extracted
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1511.05879openalex-extracted
Particular object retrieval with integral max-pooling of CNN activations
inferredcites →https://doi.org/10.48550/arxiv.1805.12177openalex-extracted
Why do deep convolutional networks generalize so poorly to small image transformations?
inferredcites →https://doi.org/10.48550/arxiv.1904.11486openalex-extracted
Making Convolutional Networks Shift-Invariant Again
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