Guojia Hou - IEEE Xplore Author Profile
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Guojia Hou

Also published under: Guo-Jia Hou

Publications
18
Citations
376
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (54)
Image of author Guojia Hou

Guojia Hou

Also published under: Guo-Jia Hou

Affiliation

College of Computer Science and Technology
Qingdao University
Qingdao, China

Publication Topics

Biography

Guojia Hou received the Ph.D. degree in computer application technology from the Ocean University of China, Qingdao, China, in 2015.,He is currently an Associate Professor with the College of Computer Science and Technology, Qingdao University, Qingdao, China. He has published more than 40 peer-reviewed articles in his research area. His current research interests include underwater vision, image/video processing, image quality assessment, and pattern recognition.(Based on document published on 28 December 2023).
Publications
18
Citations
376
Publications by Year
20142025
Co-Authors:
Show All Co-Authors (54)
Author's Published Works

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WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation

Yilin Ding;Kunqian Li;Han Mei;Shuaixin Liu;Guojia Hou

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Early Access Article |
Depth information serves as a crucial prerequisite for various visual tasks, whether on land or underwater. Recently, self-supervised methods have achieved remarkable performance on several terrestrial benchmarks despite the absence of depth annotations. However, in more challenging underwater scenarios, they encounter numerous brand-new obstacles such as the influence of marine life and degradati...Show More

WaterMono: Teacher-Guided Anomaly Masking and Enhancement Boosting for Robust Underwater Self-Supervised Monocular Depth Estimation

Yilin Ding;Kunqian Li;Han Mei;Shuaixin Liu;Guojia Hou

Year: 2025 | Early Access Article |

DAPNet: Dual Attention Probabilistic Network for Underwater Image Enhancement

Xueyong Li;Rui Yu;Weidong Zhang;Huimin Lu;Wenyi Zhao;Guojia Hou;Zheng Liang

IEEE Journal of Oceanic Engineering
Year: 2025 | Volume: 50, Issue: 1 | Journal Article |
Underwater images frequently experience issues, such as color casts, loss of contrast, and overall blurring due to the impact of light attenuation and scattering. To tackle these degradation issues, we present a highly efficient and robust method for enhancing underwater images, called DAPNet. Specifically, we integrate the extended information block into the encoder to minimize information loss d...Show More

DAPNet: Dual Attention Probabilistic Network for Underwater Image Enhancement

Xueyong Li;Rui Yu;Weidong Zhang;Huimin Lu;Wenyi Zhao;Guojia Hou;Zheng Liang

Year: 2025 | Volume: 50, Issue: 1 | Journal Article |

Global Oceanic Mesoscale Eddies Trajectories Prediction With Knowledge-Fused Neural Network

Xinmin Zhang;Baoxiang Huang;Ge Chen;Linyao Ge;Milena Radenkovic;Guojia Hou

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Efficient eddy trajectory prediction driven by multiinformation fusion can facilitate the scientific research of oceanography, while the complicated dynamics mechanism makes this issue challenging. Benefiting from ocean observing technology, the eddy trajectory dataset can be qualified for data-intensive research paradigms. In this article, the dynamics mechanism is used to inspire the design idea...Show More

Global Oceanic Mesoscale Eddies Trajectories Prediction With Knowledge-Fused Neural Network

Xinmin Zhang;Baoxiang Huang;Ge Chen;Linyao Ge;Milena Radenkovic;Guojia Hou

Year: 2024 | Volume: 62 | Journal Article |

Learning Scribbles for Dense Depth: Weakly Supervised Single Underwater Image Depth Estimation Boosted by Multitask Learning

Kunqian Li;Xiya Wang;Wenjie Liu;Qi Qi;Guojia Hou;Zhiguo Zhang;Kun Sun

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (2)
Estimating depth from a single underwater image is one of the main tasks of underwater visual perception. However, data-driven underwater depth estimation methods have long been challenging to make breakthroughs due to the difficulty in obtaining a large number of true-value references. This is partly due to the high cost of acquisition equipment, which is difficult to be applied to diverse ocean ...Show More

Learning Scribbles for Dense Depth: Weakly Supervised Single Underwater Image Depth Estimation Boosted by Multitask Learning

Kunqian Li;Xiya Wang;Wenjie Liu;Qi Qi;Guojia Hou;Zhiguo Zhang;Kun Sun

Year: 2024 | Volume: 62 | Journal Article |

GACNet: Generate Adversarial-Driven Cross-Aware Network for Hyperspectral Wheat Variety Identification

Weidong Zhang;Zexu Li;Guohou Li;Peixian Zhuang;Guojia Hou;Qiang Zhang;Chongyi Li

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (24)
Wheat variety identification from hyperspectral images holds significant importance in both fine breeding and intelligent agriculture. However, the discriminatory accuracy of some techniques is limited due to insufficient datasets, data redundancy, and noise interference. To address these issues, we propose a wheat variety identification framework called generate adversarial-driven cross-aware net...Show More

GACNet: Generate Adversarial-Driven Cross-Aware Network for Hyperspectral Wheat Variety Identification

Weidong Zhang;Zexu Li;Guohou Li;Peixian Zhuang;Guojia Hou;Qiang Zhang;Chongyi Li

Year: 2024 | Volume: 62 | Journal Article |

Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

Guojia Hou;Nan Li;Peixian Zhuang;Kunqian Li;Haihan Sun;Chongyi Li

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 2 | Journal Article |
Cited by: Papers (37)
Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform illumination underwater image restoration. Technically, the illumination channel sparsity pr...Show More

Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

Guojia Hou;Nan Li;Peixian Zhuang;Kunqian Li;Haihan Sun;Chongyi Li

Year: 2024 | Volume: 34, Issue: 2 | Journal Article |

WildFishNet: Open Set Wild Fish Recognition Deep Neural Network With Fusion Activation Pattern

Xiaoya Zhang;Baoxiang Huang;Ge Chen;Milena Radenkovic;Guojia Hou

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2023 | Volume: 16 | Journal Article |
Cited by: Papers (3)
Wild fish recognition is a fundamental problem of ocean ecology research and contributes to the understanding of biodiversity. Given the huge number of wild fish species and unrecognized category, the essence of the problem is an open set fine-grained recognition. Moreover, the unrestricted marine environment makes the problem even more challenging. Deep learning has been demonstrated as a powerfu...Show More

WildFishNet: Open Set Wild Fish Recognition Deep Neural Network With Fusion Activation Pattern

Xiaoya Zhang;Baoxiang Huang;Ge Chen;Milena Radenkovic;Guojia Hou

Year: 2023 | Volume: 16 | Journal Article |

SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement With Multi-Scale Perception

Qi Qi;Kunqian Li;Haiyong Zheng;Xiang Gao;Guojia Hou;Kun Sun

IEEE Transactions on Image Processing
Year: 2022 | Volume: 31 | Journal Article |
Cited by: Papers (92)
Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is esse...Show More

SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement With Multi-Scale Perception

Qi Qi;Kunqian Li;Haiyong Zheng;Xiang Gao;Guojia Hou;Kun Sun

Year: 2022 | Volume: 31 | Journal Article |

A Variational Framework for Underwater Image Dehazing and Deblurring

Jun Xie;Guojia Hou;Guodong Wang;Zhenkuan Pan

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2022 | Volume: 32, Issue: 6 | Journal Article |
Cited by: Papers (92)
Underwater captured images are usually degraded by low contrast, hazy, and blurry due to absorbing and scattering, which limits their analyses and applications. To address these problems, a red channel prior guided variational framework is proposed based on the complete underwater image formation model (UIFM). Unlike most of the existing methods that only consider the direct transmission and backs...Show More

A Variational Framework for Underwater Image Dehazing and Deblurring

Jun Xie;Guojia Hou;Guodong Wang;Zhenkuan Pan

Year: 2022 | Volume: 32, Issue: 6 | Journal Article |

A Hybrid Framework for Underwater Image Enhancement

Xinjie Li;Guojia Hou;Lu Tan;Wanquan Liu

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (49)
Underwater captured images often suffer from poor visibility caused by two major degradations: scattering and absorption. In this paper, we propose a hybrid framework for underwater image enhancement, which unifies underwater white balance and variational contrast and saturation enhancement. In our framework, the improved underwater white balance (UWB) algorithm is integrated with histogram stretc...Show More

A Hybrid Framework for Underwater Image Enhancement

Xinjie Li;Guojia Hou;Lu Tan;Wanquan Liu

Year: 2020 | Volume: 8 | Journal Article |

Benchmarking Underwater Image Enhancement and Restoration, and Beyond

Guojia Hou;Xin Zhao;Zhenkuan Pan;Huan Yang;Lu Tan;Jingming Li

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (52)
Image enhancement and restoration is among the most investigated topics in the field of underwater machine vision. The objective image quality assessment is a fundamental part of optimizing underwater enhancement and restoration technologies. However, most no-reference (NR) metrics are not specifically designed for underwater image quality assessment. Moreover, since the reference (undegraded) ima...Show More

Benchmarking Underwater Image Enhancement and Restoration, and Beyond

Guojia Hou;Xin Zhao;Zhenkuan Pan;Huan Yang;Lu Tan;Jingming Li

Year: 2020 | Volume: 8 | Journal Article |

An Enhanced High-Order Variational Model Based on Speckle Noise Removal With $G^0$ Distribution

Yunping Mu;Baoxiang Huang;Zhenkuan Pan;Huan Yang;Guojia Hou;Jinming Duan

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (6)
Speckle noise removal problem has been researched under the framework of regularization-based approaches. The regularizer is normally defined as total variation (TV) that induces staircase effect. Although higher-order regularizer can conquer the staircase effect to some extent, it often leads to blurred. Considering the upper questions, the combination of first and second-order regularizer will b...Show More

An Enhanced High-Order Variational Model Based on Speckle Noise Removal With $G^0$ Distribution

Yunping Mu;Baoxiang Huang;Zhenkuan Pan;Huan Yang;Guojia Hou;Jinming Duan

Year: 2019 | Volume: 7 | Journal Article |

Screen Content Image Quality Assessment With Edge Features in Gradient Domain

Ruifeng Wang;Huan Yang;Zhenkuan Pan;Baoxiang Huang;Guojia Hou

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (17)
Objective visual quality assessment specific for screen content images (SCIs) has been increasingly investigated over the years. In this paper, an effective full-reference quality evaluation model for SCIs is proposed, in which edge features in gradient domain (EFGD) are extracted for better visual perceptual representation. Unlike traditional edge feature extraction directly in the image pixel do...Show More

Screen Content Image Quality Assessment With Edge Features in Gradient Domain

Ruifeng Wang;Huan Yang;Zhenkuan Pan;Baoxiang Huang;Guojia Hou

Year: 2019 | Volume: 7 | Journal Article |

Single-color image motion deblurring using MTV model

Hong Zhang;Guodong Wang;Nan Wu;Guojia Hou;Zhimei Zhang

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Year: 2017 | Conference Paper |
Image blind restoration has been a significant subject in various application fields. In the paper, we mainly studied the color image. In the process of converting color image into gray image will result in the loss of information because color image has different channels. In order to solve blind deconvolution of color image effectively, we present a method that estimates kernel result from three...Show More

Single-color image motion deblurring using MTV model

Hong Zhang;Guodong Wang;Nan Wu;Guojia Hou;Zhimei Zhang

Year: 2017 | Conference Paper |

Color blurred image restoration based on multichannel nonlinear diffusion model

Nan Wu;Guodong Wang;Hong Zhang;Guojia Hou;Baoxiang Huang

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Year: 2017 | Conference Paper |
The blurring of the image will bring great inconvenience to recognize and analyse the content of them. In order to get rid of the blur, many scholars make a lot of contributions. In order to elevate the effect of deblurring, we propose a non-blind restoration variational model for color image deblurring based on the variational method. The proposed method uses the nonlinear diffusion equation as a...Show More

Color blurred image restoration based on multichannel nonlinear diffusion model

Nan Wu;Guodong Wang;Hong Zhang;Guojia Hou;Baoxiang Huang

Year: 2017 | Conference Paper |

A study on color model selection for underwater color image preprocessing

Guo-Jia Hou;Xin Luan;Da-Lei Song

2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
Underwater captured images suffer from quality degradation and blurring due to light absorption and scattering. Different color models combining with various preprocessing methods are used to overcome such problems, performing varying degrees of effect. Our goal is to analyze and evaluate the various color models in underwater images preprocessing. Three existing common underwater image preprocess...Show More

A study on color model selection for underwater color image preprocessing

Guo-Jia Hou;Xin Luan;Da-Lei Song

Year: 2015 | Conference Paper |

The design of ocean turbulence measurement with a Moored instrument

Xin Luan;Xiu-Yan Liu;Shu-Xin Wang;Hua Yang;Guo-Jia Hou;Da-Lei Song

OCEANS 2015 - Genova
Year: 2015 | Conference Paper |
The newly designed Instrument MTMI (Moored Turbulence Measuring Instrument) developed by OUC (Ocean University of China) had been deployed in the South China Sea from October 19, 2013 to February 10, 2014 at 21° 09.900' N 117°42.031' E and succeed to collect time series for about 115 days in 250-m-deep water. According to the requirements of turbulence measurement, the mechanical design was develo...Show More

The design of ocean turbulence measurement with a Moored instrument

Xin Luan;Xiu-Yan Liu;Shu-Xin Wang;Hua Yang;Guo-Jia Hou;Da-Lei Song

Year: 2015 | Conference Paper |

Color model selection for underwater object recognition

Dalei Song;Weicheng Sun;Zehui Ji;Guojia Hou;Xiufang Li;Liang Liu

2014 International Conference on Information Science, Electronics and Electrical Engineering
Year: 2014 | Conference Paper |
Cited by: Papers (1)
In this paper, the characteristics of underwater image and the importance of color features in underwater object recognition are presented. Classical illumination invariant color models are analyzed. Our goal is to study and evaluate the various color models in underwater object recognition applications. The illumination invariant color models yuv, c1c2c3, HSy, rSc2 and uSb are applied to carry ou...Show More

Color model selection for underwater object recognition

Dalei Song;Weicheng Sun;Zehui Ji;Guojia Hou;Xiufang Li;Liang Liu

Year: 2014 | Volume: 2 | Conference Paper |

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