Ya Huang

Xi’an University of Posts and Telecommunications

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Guijin Han

Xi’an University of Posts and Telecommunications

Xinyuan Zhang

The University of Texas Health Science Center at Houston

Wei Li

Jiangnan University

Xin Chen

Southeast University

Shiqi Gao

Third Affiliated Hospital of Harbin Medical University

Xiangfei Wu

Huawei Technologies (China)

Xiaomei Xie

University of Electronic Science and Technology of China

Wei Li

Xi’an University of Posts and Telecommunications

Cooperative Institutions

University of Electronic Science and Technology of China

Xi’an University of Posts and Telecommunications

Nanjing University of Aeronautics and Astronautics

Jiangnan University

Northwest Institute of Mechanical and Electrical Engineering

Shandong University

Zhongnan University of Economics and Law

Shenyang University of Technology

Changsha University of Science and Technology

Jiangsu Institute of Parasitic Diseases

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Synchronization and calibration of a stereo vision system

Global Oceans 2020: Singapore – U.S. Gulf Coast (2020)

Xin Chen Xiangfei Wu Shiqi Gao Xiaomei Xie Ya Huang

In this paper, we design a stereo image and video acquisition system with adjustable baseline and relative angle. Stereo vision has been used both for field robotic navigation and 3D wave reconstruction. However, there has been inadequate documentation about the synchronization performance of such system. For a dynamically moving scene, such as the wave, any delay between the two cameras will affects the accuracy of the disparity map. The present study presents a physical process to measure the delay between the two cameras. Firstly, we obtain the maximum frame rate of camera when it is triggered by external signal. Then we adopt a free fall experiment to measure the delay between trigger intervals at maximum frame rate. The relationship between baseline distance, calibration checker box tile size and target distance is discussed. Two calibration tools, i.e. MATLAB and WASS are compared using a range of distances.

Frame rate

Baseline (sea)

Stereo cameras

SIGNAL (programming language)

10.1109/ieeeconf38699.2020.9389422

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An end-to-end based on Semantic region guidance for infrared and visible image fusion

Research Square (Research Square) (2023)

Guijin Han Xinyuan Zhang Ya Huang

Abstract The goal of infrared and visible image fusion is to fuse the dominant regions in the images of the two modalities to generate high-quality fused image. However, existing methods still suffer from some shortcomings, such as lack of effective supervision information, slow computation due to complex fusion rules, and difficult convergence of GAN-based models. In this paper, we propose an end-to-end fusion method based on semantic region guidance (SRGFusion). Our model contains three basic parts: preprocessing module, image generation module, and information content discrimination module. Firstly, we input the infrared and visible images into the preprocessing module to achieve the preliminary fusion of the images. Subsequently, the features are fed into the image generation module for high-quality fused image generation. Finally, the training of the model was supervised by the information quantity discrimination module (IAQM). In particular, we improve the image generation module based on the diffusion model, which effectively avoids the design of complex fusion rules and makes it more suitable for image fusion tasks. We conduct objective and subjective experiments on four public datasets. Compared with existing methods, the fusion results of the proposed method have better objective metrics, contain more detailed information, and are more suitable for subsequent vision tasks.

Fuse (electrical)

10.21203/rs.3.rs-3154119/v1

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LIIS: Low-light image instance segmentation

Journal of Visual Communication and Image Representation (2024)

Wei Li Ya Huang Xinyuan Zhang Guijin Han

10.1016/j.jvcir.2024.104116

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Citations (1)

An end-to-end based on semantic region guidance for infrared and visible image fusion

Signal Image and Video Processing (2023)

Guijin Han Xinyuan Zhang Ya Huang

Fuse (electrical)

10.1007/s11760-023-02748-z

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TLIS: Two-stage low-light image instance segmentation

Research Square (Research Square) (2023)

Wei Li Ya Huang Xinyuan Zhang Guijin Han

Abstract Current instance segmentation models perform adequately under normal light conditions, but encounter significant challenges in low-light scenarios, where the feature information of objects becomes less discernible, thus impeding accurate segmentation. The present study proposes a two-stage methodology for instance segmentation in low-light images. In stage-I, we enhance the low-light images while denoising and enhancing the detail information. To mitigate degradation phenomena such as noise and loss of details caused by low-light image enhancement (LLIE), we propose a post-processing module for enhancing details while reducing noise. This module uses a diffusion approach with an attention mechanism to model the conditional distribution between under-enhanced and naturally illuminated images. The enhancement results from the first stage are used as input to the segmentation network in the second stage, and we observe that incorporating fine-grained features into the mask branch of the segmentation network positively affects the accurate delineation of finer masks. To achieve this objective, we devise a wavelet feature fusion module to effectively integrate the high-resolution and low-resolution features within the feature pyramid, thereby preserving more intricate details. We achieve great segmentation results on LIS, an extremely low-light instance segmentation dataset. Detailed Comparative experiments and ablation studies show the advantages and excellent generalization ability of our model.

Pyramid (geometry)

Feature (linguistics)

10.21203/rs.3.rs-3312463/v1

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