New method reduces neural image compression run-time by 50%.
problem Computational efficiency of neural image compression models.
method Automatic network optimization to reduce decoder complexity.
result Decreased decoder run-time by over 50%.
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48 results for “neural imaging”
Convolutional neural networks improve image classification accuracy.
problem Improving accuracy in image classification.
method Analyzing the convergence rate of misclassification risk for image classifiers.
result A rate of convergence independent of image dimension proves the effectiveness of CNNs.
Deep neural network improves PET image quality using inter-patient information.
problem Challenges in PET image reconstruction due to ill-posedness and limited photon detection.
method Trained a deep residual convolutional neural network in an iterative PET image reconstruction framework using ADMM.
result Proposed method outperforms neural network denoising and conventional methods in quantification.
Planar neural networks learn image transformations from sequences.
problem Learning image transformations for mental simulation.
method Using planar neural networks, the study investigates various factors affecting the learning of image transformations.
result The approach can effectively learn and transfer image transformations, including translation, rotation, and scaling.
PG-IM uses neural-symbolic programs to manipulate images.
problem Creating holistic image representations and manipulations.
method PG-IM detects patterns, induces symbolic programs, and manipulates images using a neural network.
result PG-IM learns from a single image and achieves superior performance.
Single-stage neural architecture improves text-to-image synthesis.
problem Combining text and image generation is challenging.
method Used deep residual networks and sentence interpolation.
result Achieved state-of-the-art performance with single-stage training.
A new NAS framework optimizes 3D medical image segmentation architectures.
problem Optimizing neural architectures for high-resolution 3D medical images.
method Stochastic sampling algorithm for scalable gradient-based optimization of neural connectivities and operation types in both encoder and decoder.
result Automatically designed architecture outperforms human-designed U-Net.
Neural-style algorithm classifies artistic styles of paintings.
problem Classifying artistic styles of paintings using neural networks.
method Uses neural-style algorithm to merge artistic style with content of images.
result Neural-style representation effectively classifies artistic styles of paintings.
Convolutional neural networks make astronomical image reconstruction faster and more efficient.
problem Efficiently reconstructing astronomical images from noisy or incomplete data.
method Use of convolutional neural networks for image reconstruction.
result Neural networks enable a linear complexity prediction step, making reconstruction computationally efficient.
Satellite images improve real-estate price predictions.
problem Improving accuracy of real-estate price predictions.
method Used convolutional neural networks (CNN) to incorporate satellite image data.
result CNN model trained on satellite images and structured data performs 7% better in MAE.
Neural DEs improve single image super-resolution.
problem Challenging tasks in image super-resolution.
method Applied Neural Differential Equations to image super-resolution, using variational methods and backpropagation.
result Differential models match state-of-the-art performance.
End-to-end auto-encoder for neural image compression with variable bit rates.
problem Efficient image compression with variable bit rates.
method Block-based auto-encoder system with novel contributions.
result Incremental performance improvement of each contribution.
High-contrast "gaudy" images improve the training of deep neural network models of visual cortexcs.CV
Gaudy images help train deep neural networks with less data.
problem Training deep neural networks with limited real data from visual cortex neurons.
method Used high-contrast binarized natural images (gaudy images) to train DNNs.
result Reduced training data needed for accurate DNN predictions of visual cortex neuron responses.
CURL uses neural curves to enhance global image properties.
problem Global image enhancement using neural networks.
method CURL is a multi-colour space neural retouching block trained in HSV, CIELab, and RGB color spaces.
result CURL produces state-of-the-art image quality in RGB-to-RGB and RAW-to-RGB transformations.
Untrained neural networks can recover natural images from few measurements.
problem Recovering natural images from a small number of measurements.
method Gradient descent on un-trained convolutional neural networks.
result Untrained neural networks can approximate reconstruct signals/images from a near minimal number of random measurements.
Paper explores offline and online image recognition using neural networks.
problem Challenges in evolving image recognition through different settings.
method Used Convolutional Neural Networks and Multi-layer Perceptrons.
result Encouraging preliminary results in offline and online image classification.
Convolutional neural networks handle rotated image symmetries without dimensionality issues.
problem Binary image classification with rotational symmetry.
method Least squares plug-in classifiers based on convolutional neural networks under rotationally symmetric assumptions.
result Convolutional neural networks can circumvent the curse of dimensionality in binary image classification with rotational symmetry.
3-D-CNN fusion improves high-resolution HS images from noisy MS and HS data.
problem Improving high-resolution hyperspectral images from noisy multispectral and hyperspectral data.
method 3-D-Convolutional Neural Network (3-D-CNN) for image fusion, followed by dimensionality reduction.
result The proposed method significantly reduces computational time and noise robustness compared to conventional methods.
New method speeds up image denoising models without sacrificing performance.
problem Efficiently training models for image denoising tasks.
method Introduces superkernel techniques for fast training of dense prediction models.
result Demonstrates effectiveness on SIDD+ benchmark with 6-8 RTX2080 GPU hours.
Proposes new terms for neural image compression to improve quality and efficiency.
problem Improving the quality and efficiency of neural image compression.
method Introduces a compression objective and a cycle loss term, applied to autoencoder encoder outputs, combined with reconstruction losses.
result Different autoencoders trained with varying losses produce images with distinct perceptual qualities and image-domain distortions.
Improved image recovery with minimal data using untrained neural networks.
problem Solving inverse problems with limited data.
method Pre-training neural networks with a small number of examples to improve performance.
result Performance increases as data increases, matching generative models with less than 1% of training data.
NICE framework explains CNN predictions and compresses images.
problem Interpreting deep neural networks and efficient image compression.
method End-to-end Neural Image Compression and Explanation (NICE) framework.
result NICE achieves high compression rates while maintaining classification accuracy.
Proposes a deep neural network approach for image response regression.
problem Associations between medical images and covariates.
method Spatially varying coefficient models with deep neural networks.
result Explicitly accounts for spatial smoothness and subject heterogeneity.
UNSB uses neural Schrödinger Bridge to solve unpaired image-to-image translation.
problem Difficulties in unpaired image-to-image translation with diffusion models.
method Expresses SB problem as adversarial learning problems, incorporating advanced discriminators and regularization.
result Successfully solves various unpaired image-to-image translation tasks.
New image classifier uses hierarchical max-pooling with local pooling.
problem Improving image classification accuracy with variable spatial relationships.
method Introduces a hierarchical max-pooling model with additional local pooling for convolutional neural networks.
result Demonstrates improved performance in estimating image features.
Deep Image Priorcs.CV
Randomly-initialized neural networks can capture image statistics as well as deep learning models.
problem Capturing image statistics without learning from examples.
method Using randomly-initialized neural networks as image priors.
result Randomly-initialized networks achieve similar results to deep learning models in image restoration tasks.
A tool simplifies neural network training for medical image analysis.
problem Difficulties in training neural networks for medical image analysis.
method Intuitive interface for WSI annotation and display, human-in-the-loop strategy.
result Improved network performance through iterative annotation.
Recurrent CNNs improve image classification in low light conditions.
problem Poor performance of CNNs in noisy images.
method Added recurrent connections to CNN layers to enhance robustness.
result gruCNNs outperform cCNNs in low signal-to-noise ratio images.
C3 compresses images and videos with low complexity and high performance.
problem High complexity and low performance in neural compression models.
method Overfits a small model to each image or video separately, improving RD performance with low complexity.
result Matches the RD performance of state-of-the-art neural and video codecs with significantly lower decoding complexity.
Study compares handcrafted and deep neural network features for OCT image classification.
problem Classifying OCT images for disease detection.
method Comparison of Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50.
result Deep neural network methods outperform handcrafted features with higher accuracy and better underrepresented class performance.
Efficient deep learning for CT images reduces memory and training time.
problem Training deep neural networks for CT images is computationally expensive.
method Unrolled proximal gradient descent, replaced penalty terms with CNNs, used greedy learning with deep UNet and surrogate.
result Achieved comparable image quality to state-of-the-art methods on CT image reconstruction challenges.
Study formalizes properties of deep neural networks for image recognition.
problem Understanding why deep neural networks classify images.
method Formalized properties of eight state-of-the-art deep neural networks.
result Deep neural networks can be deceived with a fooling ratio of 88%.
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networkscs.LG
mFI-PSO generates effective adversarial images for DNNs.
problem Vulnerability of DNNs to small perturbations in images.
method Uses mFI for pixel selection and PSO for objective functions.
result mFI-PSO effectively designs flexible adversarial images.
DeepBrain learns functional neural image representations for gene ontology classification.
problem Classifying gene ontology categories from neural in-situ hybridization images.
method Uses deep convolutional denoising autoencoders (CDAE) to generate compact, invariant representations.
result Improves classification accuracy by 75% to 0.98 AUC.
Deep neural network for breast cancer screening using multi-view images.
problem Improving accuracy in breast cancer detection using medical images.
method Developed a multi-view deep convolutional neural network for high-resolution medical images.
result The model achieves comparable performance to radiologists using original resolution images.
AI classifies dental X-rays using CNN, SIFT, and HOG.
problem Automating dental image analysis for efficiency and accuracy.
method Used CNN, SIFT, and HOG for image classification.
result AI algorithms accurately classified dental X-rays.
Convolutional neural networks can regularize inverse problems without training.
problem Solving inverse problems like image recovery from limited data.
method Fixed or parameterized convolutional networks with few parameters.
result Untrained convolutional networks can recover images from few measurements.
New algorithm uses untrained neural networks for image recovery, offering better compression.
problem Using untrained neural networks for image recovery and theoretical guarantees.
method Projected gradient descent scheme for solving linear and non-linear inverse problems.
result The method achieves better compression rates for the same image quality compared to hand-crafted priors.
Deep neural network improves ultrasound imaging quality.
problem Rapid performance degradation of traditional beamformers in poor data acquisition conditions.
method Developed a deep learning-based beamformer.
result Significant performance gain in contrast-to-noise ratio and structural similarity for focused and planar imaging.
Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastographyphysics.med-ph
Convolutional neural network improves MRE image reconstruction.
problem Reconstructing MRE images from displacement data is computationally intensive and costly.
method Proposes a CNN architecture to directly map MRE displacement data into elastograms, introducing a secondary loss for training.
result CNN-generated images compare favorably with nonlinear inversion methods.
Paper compresses neural network weight-updates for image artifacts removal.
problem Efficiently compressing neural network weight-updates for image artifacts removal.
method Fine-tuning a pre-trained artifact removal network on target data with a compression objective that encourages sparse and quantized weight-updates.
result Achieves reconstruction quality comparable to traditional codecs at comparable bitrates.
Super Learner outperforms other ensemble methods in deep neural network image classification.
problem Improving image classification performance using ensemble methods.
method Investigated multiple ensemble methods on deep convolutional neural networks for image classification tasks.
result Super Learner achieved the best performance across all experiments.
Convolutional neural networks converge quickly with gradient descent.
problem Learning efficient image classifiers with over-parameterized networks.
method Gradient descent for training over-parametrized CNNs with global average-pooling.
result Gradient descent quickly reduces the misclassification risk of CNNs.
BraidNet uses braid theory to optimize neural networks for image classification.
problem Image classification problems
method Procedural optimization of neural networks combining information theory and braid theory
result BraidNet outperforms other networks in learning speed and accuracy
Deep decoder generates images with few parameters, comparable to wavelets.
problem Representing and generating natural images efficiently.
method Untrained deep neural network with simple architecture (no convolutions).
result Deep decoder compresses images into concise weights, comparable to wavelet thresholding.
New method translates high-resolution images without full processing.
problem High-resolution image translation with unpaired data.
method Subsample-based training and evaluation of GANs.
result Preserves local details while maintaining global consistency.
Convolutional neural network localizes OD and fovea in UWFoV-SLO images.
problem Localizing optic disc and fovea centers in ultra-widefield retinal images.
method Convolutional neural network trained on reflectance and autofluorescence images.
result 99.4% OD localisation accuracy and 99.1% fovea localisation accuracy.
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of …