Search Results for author:
Found 30 papers, 9 papers with code
Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing
In recent years, deep learning emerges as one promising technique for solving many ill-posed inverse problems in image recovery, and most deep-learning-based solutions are based on supervised learning.
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Unsupervised Deep Unrolling Networks for Phase Unwrapping
Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging.
Neumann Network With Recursive Kernels for Single Image Defocus Deblurring
Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one.
Ground-Truth Free Meta-Learning for Deep Compressive Sampling
To further improve the learning on the null space of the measurement matrix, a modified model-agnostic meta-learning scheme is proposed, along with a null-space-consistent loss and a bias-adaptive deep unrolling network to improve and accelerate model adaption in test time.
Fingerprinting Deep Image Restoration Models
This paper proposes a fingerprinting framework for DNN models of image restoration.
Single Image Defocus Deblurring via Implicit Neural Inverse Kernels
Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs).
Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization
In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes.
Synthesis of realistic fetal MRI with conditional Generative Adversarial Networks
In the future, this algorithm would be used for generating large, synthetic datasets representing fetal brain development.
Self-Supervised Low-Light Image Enhancement Using Discrepant Untrained Network Priors
The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment.
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Self-Supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics
While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or un- supervised methods to address the challenges and costs of collecting truth images.
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount.
Ranked #4 on
Image Defocus Deblurring
on RealDOF
Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity
In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition.
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising
Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising.
Self-supervised Bayesian Deep Learning for Image Denoising
Built on the Bayesian neural network (BNN), this paper proposed a self-supervised deep learning method for denoising a single image, in the absence of training samples.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
Deep Bilateral Retinex for Low-Light Image Enhancement
Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.
Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction
We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently.
Convolutional Neural Network on Semi-Regular Triangulated Meshes and its Application to Brain Image Data
We developed a convolution neural network (CNN) on semi-regular triangulated meshes whose vertices have 6 neighbours.
Weighted total variation based convex clustering
Data clustering is a fundamental problem with a wide range of applications.
Removing out-of-focus blur from a single image
Reproducing an all-in-focus image from an image with defocus regions is of practical value in many applications, eg, digital photography, and robotics.
Estimating Defocus Blur via Rank of Local Patches
This paper addresses the problem of defocus map estimation from a single image.
Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis
Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e. g. dynamic texture (DT).
Sparse Coding for Classification via Discrimination Ensemble
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.
Removing Rain From a Single Image via Discriminative Sparse Coding
The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i. e. separate the rain layer and the de-rained image layer from an rainy image.
Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning
In addition, based on the proposed dictionary learning method, a DT descriptor is developed, which has better adaptivity, discriminability and scalability than the existing approaches.
l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence
Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem.
