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AMTrans:Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution

清华大学学报自然科学版（英文版） 2025年第3期30卷 1329-1341页

作者： Lei Gao Liyuan Cui Shuwen Chen Lizhen Deng Xiaokang Wang Xiaohong Yan Hu Zhu College of Electronic and Optical Engineering & College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer Science and Technology Hainan UniversityHaikou 570228China

Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry *** improve the quality and reliability of infrared spectroscopy signals,deconvolution is a crucial preprocessing *** by the transformer model,we propose an Auto-correlation Multi-head attention Transformer(AMTrans)for infrared spectrum sequence *** auto-correlation attention model improves the scaled dot-product attention in the *** utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation *** auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the *** proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic *** comparing the experiments with other deconvolution techniques,the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.

关键词： spectroscopy spectral deconvolution transformer auto-correlation mechanism

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Low-light image enhancement algorithm using a residual network with semantic information

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 52-62,84页

作者： Duan Lian Tang Guijin Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China

Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images,a low-light image enhancement(LIME)algorithm based on a residual network was *** algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different ***,a composite loss function was also designed for the process of low illumination image enhancement,which dynamically evaluated the loss of an enhanced image from three factors of color,structure,and *** ensures that the model can correctly enhance the image features according to the image semantics,so that the enhancement results are more in line with the human visual *** results show that compared with the state-of-the-art algorithms,the semantic-driven residual low-light network(SRLLN)can effectively improve the quality of low illumination images,and achieve better subjective and objective evaluation indexes on different types of images.

关键词： image enhancement convolutional neural network(CNN) residual learning image semantics

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Study of frame-rate up conversion based on H.264

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The Journal of China Universities of Posts and Telecommunications 2007年第1期14卷 106-110页

作者： GAN Zong-liang ZHU Xiu-chang Province Key Laboratory on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

In this study, a low complexity frame-rate up conversion method using compressed domain information for H.264 decoder is proposed. In the proposed scheme, the motion vectors （MVs） are estimated using constant acceleration motion model, and the MVs regarded as no credibility are corrected, and the interpolation method is applied on the basis of the macroblock （MB） coded types. Applied to the H.264 decoder, the proposed method provides high quality interpolation frames and an obvious decrease of the block artifacts.

关键词： video post processing H.264 motion compensated frame interpolation (MCFI) frame-rate up conversion (FRUC)

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Speaker conversion using kernel non-negative matrix factorization

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The Journal of China Universities of Posts and Telecommunications 2017年第5期24卷 60-67页

作者： Xu Qinyu Lu Guanming Yan Jingjie Li Haibo Cheng Xiao College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu Province Key Laboratory on Image Processing and Image Communication Nanjing 210003 China

Voice conversion （VC） based on Gaussian mixture model （GMM） is the most classic and common method which converts the source spectrum to target spectrum. However this method is prone to over-fitting because of its frame-by-frame conversion. The VC with non-negative matrix factorization （NMF） is presented in this paper, which can keep spectrum from over-fitting by adjusting the size of basis vector （dictionary）. In order to realize the non-linear mapping better, kernel NMF （KNMF） is adopted to achieve spectrum mapping. In addition, to increase the accuracy of conversion, KNMF combined with GMM （GKNMF） is also introduced into VC. In the end, KNMF, GKNMF, GMM, principal component regression （PCR）, PCR combined with GMM （GPCR）, partial least square regression （PLSR）, NMF correlation-based frequency warping （NMF-CFW） and deep neural network （DNN） methods are compared with each other. The proposed GKNMF gets better performance in both objective evaluation and subjective evaluation.

关键词： VC kemel NMF spectrum mapping

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A Novel Multi-Stream Fusion Network for Underwater image Enhancement

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China communications 2024年第2期21卷 166-182页

作者： Guijin Tang Lian Duan Haitao Zhao Feng Liu Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual ***, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

关键词： image enhancement multi-stream fusion underwater image

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Improved SAI method using non-local spatial constraint for image interpolation

Improved SAI method using non-local spatial constraint for i...

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2011 International Conference on Wireless communications and Signal processing, WCSP 2011

作者： Bian, Jueping Gan, Zongliang Zhang, Mengcheng Zhu, Xiuchang Jiangsu Province Key Lab on Image Processing and Image Communication NUPT Nanjing China

ISBN: (纸本)9781457710100

This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which is called non-local property. We can use this non-local strategy to improve the interpolation quality by better estimating the model parameters and Lagrangian multiplier. There are two steps in our method. In the first step, similar patches of the given block are found in the initialized high resolution image, and the model parameters can be determined properly using the expanded piecewise auto regression (PAR) model and non-local spatial constraint. In the second step, the self-similarity of patches across the high and low resolution images is exploited to solve the Lagrangian multiplier , thus to make the data estimation robust. Experiments indicate that the improved method can achieve good results both subjectively and objectively. © 2011 IEEE.

关键词： Interpolation

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Compressed Video Super-resolution Reconstruction Based on Regularized Algorithm

Compressed Video Super-resolution Reconstruction Based on Re...

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2006 8th International Conference on Signal processing

作者： Xu Zhong-qiang Gan Zongliang Zhu Xiu-chang Information Industry Ministry and Jiangsu Province Key Lab of Image Processing & Image Communication Nanjing University of Posts & Telecommunications

ISBN: (纸本)0780397371

Estimating high-resolution(HR) video from a sequence of low-resolution(LR) compressed observations is the focus of this *** on the theory of regularization,this paper proposes a new form of regularized cost function to control the within-channel balance between received data and prior information,and a channel weight coefficient to control the cross-channel *** LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated for each channel with ameliorating artifacts in compressed *** iterative gradient descent algorithm is utilized to reconstruction the HR *** results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality.

关键词： Regularization multichannel super-resolution reconstruction compressed video

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Low-light color image enhancement based on NSST

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The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 41-48页

作者： Wu Xiaochu Tang Guijin Liu Xiaohua Cui Ziguan Luo Suhuai Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Electrical Engineering and Computing the University of NewcastleNSW 2308Australia

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this *** steps of the proposed algorithm are described as ***,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value ***,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency ***,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST ***,the image is converted back to the RGB color space to obtain the enhanced *** results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

关键词： non-subsampled shearlet transform guided image filtering low-light image enhancement the HSV color space

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A NOVEL ALGORITHM OF SUPER-RESOLUTION RECONSTRUCTION FOR COMPRESSED VIDEO

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Journal of Electronics(China) 2007年第3期24卷 363-368页

作者： Xu Zhongqiang Zhu Xiuchang Information Industry Ministry and Jiangsu Province Key Lab of Image Processing ＆ Image Communication Nanjing University of Posts ＆ Telecommunications Nanjing 210003 China

Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations,which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS),this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS),a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR al-gorithm converges at a fast rate and obtains better performance in both objective and subjective quality,which is applicable for compressed video.

关键词： Super-Resolution (SR) Compressed video Projection Onto Convex Set (POCS) Quantization Constraint Set (QCS)

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A trifocal tensor calculation method based on simulated annealing algorithm

A trifocal tensor calculation method based on simulated anne...

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IET International Conference on Information Science and Control Engineering 2012, ICISCE 2012

作者： Li, Jie Liu, Feng Image Processing and Image Communication Jiangsu Key Laboratory Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781849196413

The role of trifocal tensor in three views is similar to basic matrix in two views. It includes projective geometric relations among the three views which do not depend on the structure of the scenery. According to the traditional trifocal tensor calculation. The algorithm precision relies on initial value selection and lacks robustness. The calculation is very complex but the results are not accurate. This paper puts forward a trifocal tensor calculation method based on simulated annealing algorithm. This method overcomes the problem of local minimum and initial value dependence in the majorization process by optimizing the pole coordinates and error through the method of simulated annealing algorithm, and it can get the global optimal solution. Matlab simulation experiment results show that the proposed trifocal tensor calculation method based on simulated annealing algorithm is correct and effective.

关键词： Simulated annealing

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