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Claim this profileWorks141 from public data
- 393
- Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising314
This paper proposes a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework, concatenate the RGB patches to make use of the channel redundancy, and introduces a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics.
- A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising286
This paper develops a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising and introduces three weight matrices into the data and regularization terms of the sparse coding framework to characterize the statistics of realistic noise and image priors.
- Deep Hough Transform for Semantic Line Detection248
This paper incorporates the classical Hough transform technique into deeply learned representations and proposes a one-shot end-to-end learning framework for line detection by parameterizing lines with slopes and biases, which transforms the problem of detecting semantic lines in the spatial domain into spotting individual points in the parametric domain.
- RANet: Ranking Attention Network for Fast Video Object Segmentation241
This paper proposes a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance, and develops a real-time yet very accurate Ranking Attention Network (RANet) for VOS.
- Learning to Learn with Variational Information Bottleneck for Domain Generalization206
A probabilistic meta-learning model for domain generalization is introduced, in which classifier parameters shared across domains are modeled as distributions, which enables better handling of prediction uncertainty on unseen domains.
- MobileSal: Extremely Efficient RGB-D Salient Object Detection175
This article proposes an implicit depth restoration (IDR) technique to strengthen the mobile networks’ feature representation capability for RGB-D SOD, and proposes compact pyramid refinement (CPR) for efficient multi-level feature aggregation to derive salient objects with clear boundaries.
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- Sparse, collaborative, or nonnegative representation: Which helps pattern classification?144
This paper investigates the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work and reveals that NR can boost the representation power of homogeneous samples while limiting the representationPower of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR.
- External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising142
This work proposes to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising.
- Gradient-Induced Co-Saliency Detection135
A gradient-induced co-saliency detection (GICD) method that first abstracts a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, utilizes the feedback gradient information to induce more attention to the discriminative co- salient features.
- Temporal Modulation Network for Controllable Space-Time Video Super-Resolution130
A Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction and demonstrates that the TMNet outperforms previous STVSR methods.
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- Conditional Variational Image Deraining91
A Conditional Variational Image Deraining (CVID) network is proposed for better deraining performance, leveraging the exclusive generative ability of Conditionalvariational Auto-Encoder on providing diverse predictions for the rainy image.
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- ICNet: Intra-saliency Correlation Network for Co-Saliency Detection84
An Intra-saliency Correlation Network (ICNet) is proposed to extract intra-saliency cues from the single image saliency maps (SISMs) predicted by any off-the-shelf SOD method, and obtain inter-saliency cues by correlation techniques.
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- A Simple Yet Effective Network Based on Vision Transformer for Camouflaged Object and Salient Object Detection73
A simple yet effective network (SENet) based on vision Transformer, employing a simple design of an asymmetric ViT-based encoder-decoder structure that yield competitive results on both tasks, exhibiting greater versatility than meticulously crafted ones is proposed.
- Learning deconvolutional deep neural network for high resolution medical image reconstruction71
A super resolution algorithm (SR-DCNN) is proposed for medical images that is based on a neural network and employs a deconvolution operation to effectively establish an end-to-end mapping between the low and high resolution images.
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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 2026-10-14. 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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