48 citations · 399 across the 43 of their papers we have counts for
30 papers · 1 filter
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
Yinda Chen, Wei Huang, Shenglong Zhou +2
The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron micros…
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation
Yinda Chen, Wei Huang, Xiaoyu Liu +3
Instance segmentation in electron microscopy (EM) volumes is tough due to complex shapes and sparse annotations. Self-supervised learning helps but still struggles with intricate v…
Mutual-Guided Dynamic Network for Image Fusion
Yuanshen Guan, Ruikang Xu, Mingde Yao +2
Image fusion aims to generate a high-quality image from multiple images captured under varying conditions. The key problem of this task is to preserve complementary information whi…
Domain Adaptive Synapse Detection with Weak Point Annotations
Qi Chen, Wei Huang, Yueyi Zhang +1
The development of learning-based methods has greatly improved the detection of synapses from electron microscopy (EM) images. However, training a model for each dataset is time-co…
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
Huimin Zeng, Weinong Wang, Xin Tao +3
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the sour…
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Tong Li, Hansen Feng, Lizhi Wang +2
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with…
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
Yinda Chen, Che Liu, Wei Huang +3
Vision-Language Pretraining (VLP) has demonstrated remarkable capabilities in learning visual representations from textual descriptions of images without annotations. Yet, effectiv…
Toward Real-World Light Field Super-Resolution
Zeyu Xiao, Ruisheng Gao, Yutong Liu +2
Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic down…
A Dive into SAM Prior in Image Restoration
Zeyu Xiao, Jiawang Bai, Zhihe Lu +1
The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solu…
Can SAM Boost Video Super-Resolution?
Zhihe Lu, Zeyu Xiao, Jiawang Bai +2
The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple fra…
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Huimin Zeng, Jie Huang, Jiacheng Li +1
Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate th…
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables
Jiacheng Li, Chang Chen, Zhen Cheng +1
The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a…
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
Zhen Cheng, Tao Wang, Yong Li +3
Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervis…
Recurrent Dynamic Embedding for Video Object Segmentation
Mingxing Li, Li Hu, Zhiwei Xiong +3
Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the…
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Xihao Chen, Zhiwei Xiong, Zhen Cheng +3
In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty…
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
Ruikang Xu, Mingde Yao, Chang Chen +2
Existing methods for spectral reconstruction usually learn a discrete mapping from RGB images to a number of spectral bands. However, this modeling strategy ignores the continuous…
Attribute Artifacts Removal for Geometry-based Point Cloud Compression
Xihua Sheng, Li Li, Dong Liu +1
Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, e…
Contrastive Learning for Mitochondria Segmentation
Zhili Li, Xuejin Chen, Jie Zhao +1
Mitochondria segmentation in electron microscopy images is essential in neuroscience. However, due to the image degradation during the imaging process, the large variety of mitocho…
Unfolding Taylor's Approximations for Image Restoration
Man Zhou, Zeyu Xiao, Xueyang Fu +3
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering…
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Ruikang Xu, Zeyu Xiao, Jie Huang +2
Image deblurring has seen a great improvement with the development of deep neural networks. In practice, however, blurry images often suffer from additional degradations such as do…
Unsupervised Visual Representation Learning by Tracking Patches in Video
Guangting Wang, Yizhou Zhou, Chong Luo +3
Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to…
Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Mingxing Li, Chang Chen, Xiaoyu Liu +3
Mitochondria instance segmentation from electron microscopy (EM) images has seen notable progress since the introduction of deep learning methods. In this paper, we propose two adv…
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting
Shunxin Xu, Ke Sun, Dong Liu +2
The capability of image semantic segmentation may be deteriorated due to noisy input image, where image denoising prior to segmentation helps. Both image denoising and semantic seg…
Tracking by Instance Detection: A Meta-Learning Approach
Guangting Wang, Chong Luo, Xiaoyan Sun +2
We consider the tracking problem as a special type of object detection problem, which we call instance detection. With proper initialization, a detector can be quickly converted in…