Dr. Jiajia Zhang Profile

Dr. Jiajia Zhang

at Xidian Univ

SPIE Involvement:

Author

Publications (6)

Proceedings Article | 23 January 2023 Paper

Image haze removal algorithm based on nonsubsampled contourlet transform

Ke Wang, Huixin Zhou, Huan Li, Jiajia Zhang, Sijian Hou

Proceedings Volume 12557, 125571F (2023) https://doi.org/10.1117/12.2651656

KEYWORDS: Image enhancement, Image processing, Image fusion, Image filtering, Air contamination, Image transmission, Atmospheric modeling, Image analysis

Read Abstract +

Proceedings Article | 24 November 2021 Paper

Infrared and visible image fusion via NSCT and gradient domain PCNN

Xin Zhang, Caishun Wang, Getao Chen, Jiajia Zhang, Wei Tan, Huan Li, Huixin Zhou

Proceedings Volume 12065, 120651Y (2021) https://doi.org/10.1117/12.2606088

KEYWORDS: Image fusion, Infrared imaging, Visible radiation, Infrared radiation, Image filtering, Neural networks, Modulation, Image quality, Fusion energy, Thermography

Read Abstract +

Proceedings Article | 24 November 2021 Paper

Adaptive edge detection of noisy images based on the fusion of grayscale and phase consistency

Xiang Teng, Jiajia Zhang, Huan Li, Yanyan Liu, Junxi Mei, Qingyou Yang, Zhiyu Liu, Jun Tang, Huixin Zhou

Proceedings Volume 12065, 120651F (2021) https://doi.org/10.1117/12.2605520

KEYWORDS: Image fusion, Edge detection, Detection and tracking algorithms, Image filtering, Image segmentation, Image processing algorithms and systems, Feature extraction, Convolution, Image processing, Digital filtering

Read Abstract +

Proceedings Article | 12 March 2021 Paper

Non-uniformity correction algorithm based on improved neural network

Xin Zhang, Huan Li, Juntao Hou, Dong Zhao, Huixin Zhou, Jiajia Zhang, Zhe Zhang, Kuanhong Cheng

Proceedings Volume 11763, 117632P (2021) https://doi.org/10.1117/12.2586410

Read Abstract +

Proceedings Article | 1 April 2020 Paper

Infrared and visible image fusion via NSST and PCNN in multiscale morphological gradient domain

Wei Tan, Jiajia Zhang, Pei Xiang, Huixin Zhou, William Thitøn

Proceedings Volume 11353, 113531E (2020) https://doi.org/10.1117/12.2551830

KEYWORDS: Image fusion, Infrared imaging, Visible radiation, Image processing, Infrared sensors, Neural networks

Read Abstract +

With the development of sensor technologies, imaging technology is developing more rapidly. What followed was the widespread use of image processing technology in many kinds of applications. For instance, image processing technology has been widely used in video surveillance, medical diagnosis, remote sensing detection and object tracking. As a sub-field of image processing technology, image fusion is the one of most studied technology. The aim of image fusion is to acquire an integrated image that contains more information. This integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. In all kinds of image fusion, infrared (IR) and visible (VIS) image fusion is one of the most valuable multisource image fusion. When imaging the same scene using both IR and VIS imaging system, more information can be obtained, but more redundant information is generated. The IR sensor acquires the thermal radiation information of the object in a scene, so the object can also be detected when the lighting conditions are poor. The image acquired by VIS light sensors has more spectral information, clearer texture details, and higher spatial resolution. Thus, the scene can be described more completely by integrating the IR and VIS images into one image. Meanwhile, the scene can be readily understood by observers, and the information of the scene can be easily perceived. In this paper, an effective IR and VIS image fusion via non-subsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. First, low frequency sub-image and high frequency sub-images are obtained through NSST. Then, the low frequency sub-image and high frequency sub-images are fused via a MSMG domain PCNN (MSMG-PCNN) strategy. Finally, the fused image is reconstructed by inverse NSST. Experimental results demonstrate that the proposed MSMG-PCNN-NSST algorithm performs effectively in most cases by qualitative and quantitative evaluation.

Proceedings Article | 18 December 2019 Paper

Infrared polarization image fusion via multi-scale sparse representation and pulse coupled neural network

Jiajia Zhang, Huixin Zhou, Shun Wei, Wei Tan

Proceedings Volume 11338, 113382A (2019) https://doi.org/10.1117/12.2547563

KEYWORDS: Image fusion, Infrared imaging, Infrared radiation, Polarization, Associative arrays, Neural networks, Information fusion, Neurons, IRIS Consortium, Silver

Read Abstract +

Both common information and unique information are included in the infrared polarization (IRP) images and infrared intensity (IRI) images. Aiming at the disadvantages of (1) loss of detail information; and (2) poor discrimination of fused image information, during fusion of IRP images and IRI images, a method of multi-scale sparse representation and pulse coupled neural network is proposed. A non-local means (NLM) fusion methods combined with sparse representation of image and adaptive Pulse coupled neural network (PCNN) is included in the method. Firstly, the non-local means filter is used to obtain the image information of the source image at different scales. Secondly, a non-subsampled directional filter bank (NSDFB) is used to decompose the high-frequency information of different scales into multiple highfrequency direction sub-bands. For multiple high-frequency directions, the spatial frequency (SF) transformation is first performed for multiple high frequency direction sub-bands, and the PCNN is used to obtain the high frequency subbands fused image according to its significance, where the link strength of PCNN is adaptively adjusted by region variance. Then, the joint matrix composed with the low-frequency components is trained by K-singular value decomposition method (K-SVD) to get the redundant dictionary. The common information and unique information are judged by the position information of non-zero values in the sparse coefficient, and are fused with different methods. Finally, the fused high and low frequency sub-bands are inversely transformed by a non-negative matrix to obtain a fused image. Experimental results demonstrate that the proposed fusion algorithm can not only highlight the common information of the source image, but also retain their unique information. Meanwhile, the fused image has higher contrast and detail information. In addition, the fused image performs well in terms of average gradient (AG), edge intensity (EI), information entropy (IE), standard deviation (STD), spatial frequency (SF) and image definition (IDEF).

Showing 5 of 6 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years