Search Results for author: Jiaming Liu

Found 25 papers, 6 papers with code

Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping

no code implementations3 Sep 2021 Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu, Sayan Kahali, Weijie Gan, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov

LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.

MoDIR: Motion-Compensated Training for Deep Image Reconstruction without Ground Truth

no code implementations12 Jul 2021 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.

Image Reconstruction

Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

no code implementations7 Jun 2021 Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek S. Kamilov

The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.

Compressive Sensing Denoising

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

1 code implementation22 Jan 2021 Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov

Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.

Contextual Graph Reasoning Networks

no code implementations1 Jan 2021 Zhaoqing Wang, Jiaming Liu, Yangyuxuan Kang, Mingming Gong, Chuang Zhang, Ming Lu, Ming Wu

Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.

Instance Segmentation Pose Estimation +1

Joint Reconstruction and Calibration using Regularization by Denoising

no code implementations26 Nov 2020 Mingyang Xie, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.

Denoising Image Reconstruction

Practical Deep Raw Image Denoising on Mobile Devices

no code implementations ECCV 2020 Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang

Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.

Image Denoising

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

no code implementations ICLR 2021 Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek S. Kamilov

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.


Deep Image Reconstruction using Unregistered Measurements without Groundtruth

no code implementations29 Sep 2020 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.

Image Reconstruction

Provable Convergence of Plug-and-Play Priors with MMSE denoisers

no code implementations15 May 2020 Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.

Compressive Sensing Image Reconstruction

FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images

no code implementations15 Mar 2020 Kaiyan Chen, Ming Wu, Jiaming Liu, Chuang Zhang

To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.

Infusing Learned Priors into Model-Based Multispectral Imaging

no code implementations20 Sep 2019 Jiaming Liu, Yu Sun, Ulugbek S. Kamilov

We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.

Denoising Image Reconstruction

EATEN: Entity-aware Attention for Single Shot Visual Text Extraction

1 code implementation20 Sep 2019 He guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding

Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.

Entity Extraction using GAN Optical Character Recognition

Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning

no code implementations ICCV 2019 Yipeng Sun, Jiaming Liu, Wei Liu, Junyu Han, Errui Ding, Jingtuo Liu

Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data.

Disentangled Image Matting

no code implementations ICCV 2019 Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, Jian Sun

Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.

Image Matting

Online Regularization by Denoising with Applications to Phase Retrieval

no code implementations4 Sep 2019 Zihui Wu, Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.


Editing Text in the Wild

2 code implementations8 Aug 2019 Liang Wu, Chengquan Zhang, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai

Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.

Image Inpainting Image-to-Image Translation +1

Semi-supervised Skin Detection by Network with Mutual Guidance

no code implementations ICCV 2019 Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, Jue Wang

In this paper we present a new data-driven method for robust skin detection from a single human portrait image.

Block Coordinate Regularization by Denoising

1 code implementation NeurIPS 2019 Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.


Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

no code implementations8 May 2019 Yifan Ding, Chuan Wang, Haibin Huang, Jiaming Liu, Jue Wang, Liqiang Wang

Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.

Image Inpainting Optical Flow Estimation +1

Detecting Text in the Wild with Deep Character Embedding Network

no code implementations2 Jan 2019 Jiaming Liu, Chengquan Zhang, Yipeng Sun, Junyu Han, Errui Ding

However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches.

TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network

no code implementations24 Dec 2018 Yipeng Sun, Chengquan Zhang, Zuming Huang, Jiaming Liu, Junyu Han, Errui Ding

Reading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text.

Optical Character Recognition

Image Restoration using Total Variation Regularized Deep Image Prior

no code implementations30 Oct 2018 Jiaming Liu, Yu Sun, Xiaojian Xu, Ulugbek S. Kamilov

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.

Deblurring Image Denoising +2

Cannot find the paper you are looking for? You can Submit a new open access paper.