A5100319450- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Image Processing Techniques and Applications
- Image and Video Quality Assessment
- Generative Adversarial Networks and Image Synthesis
- Image Enhancement Techniques
- Image and Signal Denoising Methods
- Advanced Image Fusion Techniques
- Photoacoustic and Ultrasonic Imaging
- Cancer Mechanisms and Therapy
- Galectins and Cancer Biology
- Multimodal Machine Learning Applications
- Signaling Pathways in Disease
- RNA modifications and cancer
- Face recognition and analysis
- interferon and immune responses
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- COVID-19 diagnosis using AI
- Heat shock proteins research
- Extraction and Separation Processes
- Higher Education Practises and Engagement
- Advanced Optical Sensing Technologies
- Cell Image Analysis Techniques
- Human Motion and Animation
Chinese People's Liberation Army
2020-2024
Jilin University
2024
Beijing University of Posts and Telecommunications
2024
Beijing Academy of Artificial Intelligence
2023
Chinese Academy of Sciences
2023
Nanjing Medical University
2023
Shanghai Artificial Intelligence Laboratory
2023
Shenzhen Institutes of Advanced Technology
2023
Soochow University
2023
First Affiliated Hospital of Soochow University
2023
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for denoising, especially with emergence of Transformer-based models that have achieved notable state-of-the-art results on various tasks. However, deep learning-based methods often suffer from lack generalization ability. For example, trained Gaussian may perform poorly when tested other distributions. To address...
The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest Transformers, are generally equipped with well-designed modules. However, progress self-attention mechanism may violate this common sense. In paper, we rethink role Transformers and make several counter-intuitive observations. Our experiments show that: (i) can directly utilize multi-frame information from unaligned videos, (ii) existing methods...
With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising performance in linguistic quality description. However, current still fall short accurately scoring image quality. In this work, we aim to leverage MLLMs regress accurate scores. A key challenge is that score inherently continuous, typically modeled as a Gaussian distribution, whereas generate discrete token outputs. This mismatch necessitates...
In recent years, deep learning has made great progress in many fields such as image recognition, natural language processing, speech recognition and video super-resolution. this survey, we comprehensively investigate 33 state-of-the-art super-resolution (VSR) methods based on learning. It is well known that the leverage of information within frames important for Thus propose a taxonomy classify into six sub-categories according to ways utilizing inter-frame information. Moreover,...
The recurrent structure is a prevalent framework for the task of video super-resolution, which models temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, states tend contain unpleasant artifacts propagate them restored frames. In this circumstance, our analyses show that such can be largely alleviated when state replaced cleaner counterpart. Based on observations, we propose Hidden State Attention (HSA) module...
Transformer-based methods have shown impressive performance in image restoration tasks, such as super-resolution and denoising. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies the potential Transformer is still not fully exploited existing networks. In order to activate more pixels for better restoration, propose new Hybrid Attention (HAT). It combines both channel attention window-based...
Metabolic disorders and oxidative stress are the main causes of diabetic cardiomyopathy. Activation nuclear factor erythroid 2-related 2 (Nrf2) exerts a powerful antioxidant effect prevents progression However, mechanism its cardiac protection direct action on cardiomyocytes not well understood. Here, we investigated in cardiomyocyte-restricted Nrf2 transgenic mice (Nrf2-TG) DCM mechanism. In this study, were used to directly observe whether cardiomyocyte-specific overexpression can prevent...
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies the potential Transformer is still not fully exploited existing networks. In order to activate more pixels for better reconstruction, propose novel Hybrid Attention (HAT). It combines both channel attention and window-based...
This study aimed to evaluate the effect of rain-shelter on microbial diversity in spontaneous fermentation Cabernet Sauvignon wines, and correlations core taxa with biological parameters monomeric phenols, an open-field mode as control. Firstly, increased contents 2.6% alcohol, 6.8% glycerol, 10.3% malic acid 20.3% catechins resulting respectively (P < 0.05). As advanced, bacterial decreased then increased, while fungal 0.05), regardless cultivation. Besides, significant were identified...
Text-based style transfer is a newly-emerging research topic that uses text information instead of image to guide the process, significantly extending application scenario transfer. However, previous methods require extra time for optimization or text-image paired data, leading limited effectiveness. In this work, we achieve data-efficient text-based method does not at inference stage. Specifically, convert input space pre-trained VGG network realize more effective swap. We also leverage...
Although evidence supports an observational association between tea consumption and susceptibility to head neck cancer, the causal nature of this remains unclear.
Blind image super-resolution (SR), aiming to super-resolve low-resolution images with unknown degradation, has attracted increasing attention due its significance in promoting real-world applications. Many novel and effective solutions have been proposed recently, especially the powerful deep learning techniques. Despite years of efforts, it still remains as a challenging research problem. This paper serves systematic review on recent progress blind SR, proposes taxonomy categorize existing...
Most existing image restoration methods use neural networks to learn strong image-level priors from huge data estimate the lost information. However, these works still struggle in cases when images have severe information deficits. Introducing external or using reference provide also limitations application domain. In contrast, text input is more readily available and provides with higher flexibility. this work, we design an effective framework that allows user control process of degraded...
Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed individual tasks designing such requires distinct expertise. Building upon the success of large language (LLMs) natural (NLP), there similar trend focuses on developing large-scale through pretraining in-context learning. This paradigm shift reduces reliance models, yielding...
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks both civil and military fields. Empowering UAVs intelligence by artificial (AI) especially machine learning (ML) techniques is inevitable appealing enable the aforementioned applications. To solve problems of traditional cloud-centric ML for UAV such as privacy concern, unacceptable latency, resource burden, a distributed technique, \textit(i.e.), federated...
Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details. However, it is notorious that GAN-based SR models will inevitably produce unpleasant and undesirable artifacts, especially practical scenarios. Previous works typically suppress artifacts an extra loss penalty the training phase. They only work for in-distribution artifact types generated during training. When applied real-world scenarios, we observe those...
Consistent Hashing (CH) algorithms are widely adopted in networks and distributed systems for their ability to achieve load balancing minimize disruptions. However, the rise of Internet Things (IoT) has introduced new challenges existing CH algorithms, characterized by high memory usage update overhead. This article presents DxHash, a novel algorithm based on repeated pseudo-random number generation. DxHash offers significant benefits, including remarkably low footprint, lookup throughput,...
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus proposed solutions and results. The task was to super-resolve an input a magnification factor x4 based set of prior examples low corresponding high resolution images. goal is devise network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, memory consumption while at least maintaining PSNR MSRResNet. track had 150 registered participants, 25 teams submitted...
Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations. In this work, we present a new 2D animation visual dataset, AnimeRun, by converting open source three-dimensional (3D) movies full scenes in style, including simultaneous moving background interactions of multiple subjects. Our analyses show that the proposed dataset not only resembles anime more image...
This paper reports on the NTIRE 2022 challenge perceptual image quality assessment (IQA), held in conjunction with New Trends Image Restoration and Enhancement workshop (NTIRE) at CVPR 2022. is to address emerging of IQA by processing algorithms. The output images these algorithms have completely different characteristics from traditional distortions are included PIPAL dataset used this challenge. divided into two tracks, a full-reference track similar previous new that focuses no-reference...
Image super-resolution (SR) is a representative low-level vision problem. Although deep SR networks have achieved extraordinary success, we are still unaware of their working mechanisms. Specifically, whether can learn semantic information, or just perform complex mapping function? What hinders from generalizing to real-world data? These questions not only raise our curiosity, but also influence network development. In this paper, make the primary attempt answer above fundamental questions....