Huimin Lu

Associate Professor

Kyushu Institute of Technology

Japan

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About

Huimin Lu is an Associate Professor at the Kyushu Institute of Technology, Japan. His research encompasses various areas including underwater image enhancement, deep learning algorithms, and multimodal sentiment analysis, as highlighted in his recent publications. Noteworthy articles include studies on object detection in underwater settings and advancements in video enhancement techniques.

Articles (20)

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2022

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Huimin Lu

Research Interests

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About

Articles (20)

Underwater Optical Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future

High-Turbidity Underwater Image Enhancement via Turbidity Suppression Fusion

SPADesc: Semantic and parallel attention with feature description

DAPNet: Dual Attention Probabilistic Network for Underwater Image Enhancement

Joint Objective and Subjective Fuzziness Denoising for Multimodal Sentiment Analysis

Adaptive Network Slicing and Hierarchical Data Management for Edge-Assisted Multi-Task Video Enhancement

Attention-Guided Multiscale Interaction Network for Face Super-Resolution

Turbid Underwater Image Enhancement with Illumination-constrained and Structure-preserved Retinex Model

Underwater Image Enhancement via Wavelet Decomposition Fusion of Advantage Contrast

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning

Lightweight Feature De-redundancy and Self-calibration Network for Efficient Image Super-resolution

Edge Computing With Complementary Capsule Networks for Mental State Detection in Underground Mining Industry

Multiscale Shared Learning for Fault Diagnosis of Rotating Machinery in Transportation Infrastructures

Context-Patch Representation Learning With Adaptive Neighbor Embedding for Robust Face Image Super-Resolution

Depth-Distilled Multi-Focus Image Fusion

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

JDSR-GAN: Constructing an Efficient Joint Learning Network for Masked Face Super-Resolution

Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN

Editorial Introduction to Responsible Artificial Intelligence for Autonomous Driving

Action Recognition Framework in Traffic Scene for Autonomous Driving System

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