48 citations · 399 across the 43 of their papers we have counts for
44 papers
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…
Deep Multi-Threshold Spiking-UNet for Image Processing
Hebei Li, Yueyi Zhang, Zhiwei Xiong +1
U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper…
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…
TridentSE: Guiding Speech Enhancement with 32 Global Tokens
Dacheng Yin, Zhiyuan Zhao, Chuanxin Tang +2
In this paper, we present TridentSE, a novel architecture for speech enhancement, which is capable of efficiently capturing both global information and local details. TridentSE mai…
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
Gang Yang, Li Zhang, Man Zhou +4
Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed information for accurate diagnosis and quantitative image analysis. Despite the significant advanc…
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
Dacheng Yin, Chuanxin Tang, Yanqing Liu +6
This paper proposes a new "decompose-and-edit" paradigm for the text-based speech insertion task that facilitates arbitrary-length speech insertion and even full sentence generatio…
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…
aiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform
Dongmei Xue, Haichuan Ma, Li Li +2
Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly…
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Dacheng Yin, Xuanchi Ren, Chong Luo +3
This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a…
Retinal Vessel Segmentation with Pixel-wise Adaptive Filters
Mingxing Li, Shenglong Zhou, Chang Chen +3
Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast. Previous methods generally refine segmentation resul…
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…