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Claim this profileWorks138 from public data
- DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal138
This paper explores the cross-scale manner between networks and inner-scale fusion operation to solve the image rain removal task and proposes a multi-sub-networks structure, which outperforms over the state-of-the-art methods.
- GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning126
This paper proposes a novel privacy-preserved architecture where clients can collaboratively train a deep model while preserving the privacy of each client’s data by carefully partition a deep neural network to two non-colluding parties.
- Deterministic Learning Theory117
Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments and represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).
- Correlation Matching Transformation Transformers for UHD Image Restoration102
Experimental results show that the UHDformer reduces about ninety-seven percent model sizes compared with most state-of-the-art methods while significantly improving performance under different training sets on 3 UHD image restoration tasks, including low-light image enhancement, image dehazing, and image deblurring.
- Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network82
An effective algorithm, called JDNet, is proposed to solve the single image deraining problem and conduct the segmentation and detection task for applications and has better deraining results both on real-world and synthetic datasets.
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- Command filter based adaptive neural trajectory tracking control of an underactuated underwater vehicle in three-dimensional space75
The stability of the control scheme proposed in this paper is proved in the sense of Lyapunov stability theory after comparative simulations were carried out to illustrate the effectiveness and good robustness of the proposed tracking controller.
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- S-Box Design Based on 2D Multiple Collapse Chaotic Map and Their Application in Image Encryption36
A new chaotic S-box was constructed based on 2D-MCCM, and a diffusion method was designed based on the S- box, which improved security and efficiency and a new image encryption algorithm was proposed.
- Physical Model Guided Deep Image Deraining35
A novel network based on physical model guided learning for single image deraining, which consists of three sub-nets: rain streaks network, rain-free network, and guide-learning network, which outperforms the state-of-the-art deraining methods.
- Online-Updated High-Order Collaborative Networks for Single Image Deraining32
A high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module to exploit features from deep networks externally and internally for rain streaks removal and an online-update learning approach, which uses real-world rainy images to fine-tune the network and update the deraining results in a self-supervised manner.
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- Intelligent Grading of Tobacco Leaves Using an Improved Bilinear Convolutional Neural Network27
This work developed assembly line equipment and an RGB image classification method for tobacco grading that uses a pyramid structure, attention mechanism, and angle decision loss function to improve the bilinear convolutional neural network (a fine-grained visual categorization framework) for Tobacco grading.
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- Crypto primitive of MOCVD MoS2 transistors for highly secured physical unclonable functions26
The generated PUF keys exhibit good randomness and uniqueness, providing a possibility for harvesting highly secured PUF devices with two-dimensional materials.
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 2026-10-15. 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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