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Works126 from public data
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- AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system538
An AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia and is able to overcome a series of challenges in this particular situation and deploy the system in four weeks.
- Attention-Guided Network for Ghost-Free High Dynamic Range Imaging313
The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error, and can achieve state-of-the-art quantitative and qualitative results.
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- HVI: A New Color Space for Low-light Image Enhancement244
A new color space for LLIE is proposed, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity, defined by polarized HS maps and learnable intensity.
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- Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation203
A novel Multi-scale Residual Encoding and Decoding network (Ms RED) for skin lesion segmentation, which is able to accurately and reliably segment a variety of lesions with efficiency, and introduces a novel pooling module to medical image segmentation for the first time.
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- I 2 U-Net: A dual-path U-Net with rich information interaction for medical image segmentation162
Extensive experiments on four challenging tasks, including skin lesion, polyp, brain tumor, and abdominal multi-organ segmentation, consistently show that the proposed I2U-Net has superior performance and generalization ability over other state-of-the-art methods.
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- Toward High-Quality HDR Deghosting With Conditional Diffusion Models121
The HDR deghosting problem is formulated as an image generation that leverages LDR features as the diffusion model’s condition, consisting of the feature condition generator and the noise predictor and the noise predictor achieves state-of-the-art performances and well generalization to real-world images.
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- COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations109
A new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections is established and achieves state-of-the-art performance.
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- Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts96
A novel method called STPrompt that learns spatio-temporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) achieves state-of-the-art performance on three public benchmarks for the WSVADL task.
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- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
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- Deep Learning for Video Anomaly Detection: A Review80
An extensive and comprehensive research review of VAD tasks, covering the spectrum of five different categories, namely, semi-supervised, weakly supervised, fully supervised, unsupervised, and open-set supervised VAD, and delve into the latest VAD works based on pretrained large models and open-world learning.
- ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution73
This research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution), which surpasses previous attempts in qualitative and quantitative results.
- High dynamic range imaging by sparse representation72
A ghost-free HDR image synthesis algorithm that utilizes a sparse representation framework based on the dependency among adjacent low dynamic range images and the sparsity of the moving object that leads to the ghost artifacts is proposed.
- Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning56
A Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers and achieves state-of-the-art result for mitigating forgetting.
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- Dual-Attention-Guided Network for Ghost-Free High Dynamic Range Imaging51
This work proposes a novel dual-attention-guided end-to-end deep neural network, called DAHDRNet, which produces high-quality ghost-free HDR images and employs a hybrid loss function, which consists of a perceptual loss, a total variation loss, and a content loss to recover photo-realistic images.
- A Unified HDR Imaging Method with Pixel and Patch Level50
A hybrid HDR deghosting network, called HyHDRNet, is proposed to learn the complicated relationship between reference and non-reference images and outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.
- Attention-Based Network For Low-Light Image Enhancement48
A novel attention-based neural network to generate high-quality enhanced low-light images from the raw sensor data and proposes a new pooling layer, called inverted shuffle layer, which adaptively selects useful information from previous features.
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- NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results45
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022, and the competition set-up, datasets, proposed methods and their results.
- Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition41
Inspired by the notion that high/low frequency information is applicable to different degradations, HLNet is introduced, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition, thus achieving higher-quality image restoration.
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- Generating Content for HDR Deghosting from Frequency View40
The Low-Frequency aware Diffusion (LF-Diff) model is proposed, implementing the DMs in a highly compacted latent space and integrating it into a regression-based model to enhance the details of reconstructed images.
- CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task40
The Composite Refinement Network (CRNet) is proposed, which can perform unified image restoration and enhancement on multiple exposure images using multiple exposure images, and explicitly separates and strengthens high and low-frequency information through pooling layers, using specially designed Multi-Branch Blocks.
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- High dynamic range imaging via gradient-aware context aggregation network38
The proposed HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain.
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- NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results30
A comprehensive overview of the NTIRE 2025 Efficient Burst HDR and Restoration Challenge is provided, the proposed solutions are compared, and it serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.
- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results29
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
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- Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation10
This paper tackles the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame, as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples.
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- HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement4
This work proposes HVI-CIDNet+, a Color and Intensity Decoupling Network designed to restore degraded content and mitigate color distortion in extremely dark regions, and introduces a Region Refinement Block that combines convolution for information-rich regions with attention for information-scarce regions, enabling accurate brightness adjustment.
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- DCWNet: Bridging Dual-Codebook Reconstruction and Wavelet Refinement for Low-Light Image Enhancement–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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