New method reconstructs signals and images from periodic nonlinearities.
problem Reconstructing signals and images from periodic nonlinearities.
method Design of measurement scheme for efficient reconstruction, adaptable to compressive sensing.
result Effective reduction in measurement complexity for HDR imaging with minimal quality loss.
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that di…
Paper introduces PTL-SI for statistical inference in TL-HDR, controlling FPR.
problem Quantifying statistical significance in TL-HDR with limited data.
method PTL-SI framework for valid p-values in TL-HDR feature selection. result Valid p-values and controlled FPR in TL-HDR feature selection. Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In th…
We solve hard Gaussian integrals efficiently using geometry and sampling.
problem Hard computation of integrals over linearly constrained Gaussian densities.
method Combines Holmes-Diaconis-Ross method with elliptical slice sampling adapted to linear settings.
result Direct computation of extremely small probability masses.
New methods for predicting compositional data using conformal prediction.
problem No well-established methods for constructing valid prediction sets in compositional data.
method Investigated three conformal prediction-based approaches for Dirichlet regression models.
result HDR approximation approach is robust in terms of coverage, while grid discretization reduces overcoverage.
New ML approach predicts glare in open-plan offices with high accuracy.
problem Challenging to predict discomfort glare in open-plan offices.
method Used Machine Learning algorithms to predict glare.
result ML model achieved 83.8% accuracy in predicting glare.
Develops a method for stress testing correlations of financial portfolios.
problem Stress testing correlations in financial asset portfolios.
method Parametric representation of correlations, Bayesian variable selection, joint distribution of stress scenarios.
result Inference of worst-case correlation scenarios using stress tests.
New method predicts spatial events like hurricanes and earthquakes with uncertainty.
problem Quantifying uncertainty in natural hazard predictions.
method Representing spatial point clouds as empirical measures, constraining prediction sets to spatial data manifold, using Wasserstein distance.
result Achieves near-nominal coverage and lower energy/manifold distances compared to baselines.
Develops conformal Bayes for two-sided censored Gaussian regression under label shift.
problem Prediction under label shift with censored responses.
method Combines posterior predictive tilting with weighted conformal calibration.
result Restores marginal coverage with smaller prediction sets.
The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations…
CatSIM measures image similarity robustly to small changes.
problem Measuring similarity between images, especially with small perturbations.
method Uses structural similarity image quality paradigm, robust to small location changes.
result Structural similarity between images rated higher when not entirely overlapping.
Develops statistical guarantees for image-to-image regression models.
problem Current image-to-image regression models lack statistical guarantees for model mistakes and hallucinations.
method Uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems.
result Derives uncertainty intervals around each pixel with formal mathematical guarantees.
AugurOne trains single image generators without GANs using image warps.
problem Training end-to-end single image generators without GANs.
method Non-affine augmentations of single input images for training an upscaling neural network.
result End-to-end training yields state-of-the-art performance on conditional generation tasks.
Image visibility graphs map images into graphs for processing and classification.
problem Mapping image structures into graphs for processing and classification.
method Introduced image visibility graphs (IVGs) and explored their use in image processing and classification.
result IVGs encapsulate relevant image structure information and are computationally efficient.
Paper analyzes learning from ghost imaging without reconstruction bottleneck.
problem High-speed cell classification bottleneck in ghost cytometry.
method Theoretical analysis of learning from ghost imaging without reconstruction.
result Theoretical analysis supports learning from ghost imaging without reconstruction.
Total variation denoising improves image quality adaptively.
problem Improving image quality from noisy data.
method Total variation regularization for image denoising.
result Denoised images converge to true images at a parametric rate.
Improves text-to-image generation with bidirectional capabilities.
problem Generating realistic images from text descriptions.
method Integrates text and image modalities using MMVR architecture with n-gram cost function and multiple sentences.
result Significant improvement in image quality over existing methods (over 20%).
Transmission imaging, as an important imaging technique widely used in astronomy, medical diagnosis, and biology science, has been shown in [49] quite different from reflection imaging used in our everyday life. Understanding the structures of images (the prior information) is important for designing, testing, and choo…
This paper tackles generating manifold-valued images using WGAN.
problem Generating manifold-valued images over natural images.
method Formulated a theorem of optimal transport for Wasserstein distance on manifolds, introduced a new WGAN framework.
result Proposed model generates more plausible manifold-valued images than competitors.
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
This study investigates how much knowledge from natural images can be transferred to pathology images.
problem Quantifying how much knowledge from natural images can be transferred to pathology images.
method Proposes a framework to quantify knowledge gain by a particular layer, conducts empirical investigation in pathology image centered transfer learning.
result Early layers of deep models can transfer knowledge to pathology image classification tasks.
Generates high-resolution images from low-resolution inputs.
problem Generating realistic images from low-resolution inputs.
method Latent Adversarial Generator (LAG) using perceptual loss.
result Samples of high-resolution images from low-resolution inputs.
BCD-Net uses identical CNN structures for image recovery in undersampled imaging.
problem Challenges in obtaining accurate images from undersampled or noisy measurements.
method Incorporates image mapping CNN into BCD signal recovery method using alternating direction method of multipliers.
result Significantly more accurate image recovery compared to existing methods.
Few-shot unsupervised image-to-image translation model learns from a few examples.
problem Current unsupervised image-to-image translation methods require many images at training time.
method Coupling adversarial training with a novel network design for few-shot learning.
result Model achieves effective few-shot image-to-image translation.
Generates synthetic laparoscopic images for training deep neural networks.
problem Lack of large labeled data sets for laparoscopic image processing.
method Unpaired image-to-image translation to generate realistic synthetic images.
result Synthetic data set improves liver segmentation performance without manual labeling.
Paper proposes a CNN-LSTM model for image denoising and reconstruction.
problem Challenging task of image denoising and reconstruction in computer vision.
method Proposes an encoder-decoder model with direct attention, using CNN for encoding and LSTM for decoding.
result Model can reconstruct clean images from highly corrupted ones, even when human understanding is difficult.
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
Generative Adversarial Networks optimize model parameters for image matching.
problem Optimizing model parameters for accurate image matching.
method Model-Assisted Generative Adversarial Network (GAN) to produce fake images matching true images.
result Best match model parameter values can minimize bias in image recognition.
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.
Paper introduces LLISE for image structure learning using SSIM.
problem Image quality assessment using MSE or ℓ2 norm is not promising. method Locally Linear Image Structural Embedding (LLISE) using SSIM.
result LLISE captures image structure features and discriminates distortions.
Paper proposes method to generate images from text using GANs trained on uncaptioned images.
problem Limited captioned image datasets for text-to-image synthesis.
method Conditional GANs trained on uncaptioned images with an Image Captioning Module.
result Promising preliminary results compared to unconditional GANs.
Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling tra…
The paper analyzes relationships between various image models for restoration.
problem Unclear relationships among popular image models for restoration.
method Theoretical analysis and experimental study of image models.
result Improved denoising performance by combining multiple image models.
Framework translates images between domains without supervision.
problem Challenges in unsupervised image-to-image translation, especially handling multimodality.
method Proposes a Multimodal Unsupervised Image-to-Image Translation (MUNIT) framework, decomposing images into content and style codes.
result Demonstrates improved generation of diverse outputs from a single source image.
Deep models generate images with missing high-frequency details.
problem Malicious use of realistic deep-generated images.
method Analysis of Fourier spectrum discrepancies between real and generated images.
result Detection method achieves up to 99.2% accuracy in classifying real and generated images.
StrokeCoder uses Transformers to generate images from single examples.
problem Creating diverse images from a single example.
method Transformer Neural Network learns from a single path-based example to generate a set of images.
result The model can generate a large set of deviated images that still represent the original image's style and concept.
Deep learning enhances optical microscopy and image reconstruction.
problem Improving image data transformations in optical microscopy.
method Application of deep learning methods on optical microscopy and image reconstruction.
result Deep learning enables new transformations among different modes and modalities of microscopic imaging.
New method reconstructs images from fMRI data using unlabeled data.
problem Challenges in acquiring labeled data for fMRI-to-image reconstruction.
method Self-supervised training with Encoder-Decoder and Decoder-Encoder networks.
result Reconstruction network adapts to new unlabeled test data.
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
Style generators can invertibly generate images, useful for image enhancement and animation.
problem Generating all possible images and maintaining invertibility are challenging for GANs.
method Proposed style generators are invertible and can generate all possible images.
result Style generators outperform other GANs and Deep Image Prior as image priors.
Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from da…
Two-layer model sparsifies image residuals for CT image reconstruction.
problem Image reconstruction from limited and corrupted data.
method Pre-learning a two-layer sparsifying transform model with block coordinate descent optimization.
result Preliminary experiments show the two-layer model improves CT image reconstruction from low-dose measurements.
Survey of deep learning methods for image captioning.
problem Generating accurate and complex image descriptions.
method Comprehensive review of deep learning techniques for image captioning.
result Analysis of strengths, limitations, and popular datasets in deep learning image captioning.
CollaGAN uses GANs to impute missing image data.
problem Missing data bias in applications requiring multiple inputs.
method Collaborative Generative Adversarial Network (CollaGAN) for multi-domain image imputation.
result CollaGAN produces higher quality imputed images than existing methods.
Project analyzes images' impact on sentiment analysis.
problem Understanding how images contribute to sentiment classification.
method Compared models using only images, only text, or both.
result Combined models improved sentiment classification accuracy.
OTRE uses OT to improve retinal images, outperforming existing methods.
problem Improving quality of non-mydriatic retinal images for accurate diagnoses.
method OT theory for image-to-image translation, regularization by enhancing.
result OTRE outperforms state-of-the-art methods on various retinal image tasks.