A New Method for Improving Infrared Image Quality and its System Implementation

To obtain high-quality infrared image real-timely, a new correction enhancement method is proposed. The method can both compensate nonuniformity of IRFPA by using calibration-based piecewise polynomial interpolation correction algorithm and increase image contrast by using histogram-based adaptive threshold image enhancement algorithm. The experiment is performed by carrying out the method in an embedded imaging system. The results show that the system can process infrared image real-timely and the processed image is clear with high signal to noise ratio.

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Design of imaging system of cloud and aerosol polarization imager with high signal-to-noise ratio

Infrared and Laser Engineering ◽

10.3788/irla201847.1111006 ◽

2018 ◽

Vol 47 (11) ◽

pp. 1111006

Author(s):

李帅 Li Shuai ◽

徐抒岩 Xu Shuyan ◽

刘栋斌 Liu Dongbin ◽

张航 Zhang Hang

Keyword(s):

Imaging System ◽

Signal To Noise Ratio ◽

High Signal ◽

Signal To Noise ◽

Noise Ratio

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Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

Applied Sciences ◽

10.3390/app9214624 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4624

Author(s):

Uzokboy Ummatov ◽

Kyungchun Lee

Keyword(s):

Mimo Systems ◽

Signal To Noise Ratio ◽

Multiple Input Multiple Output ◽

Threshold Value ◽

Adaptive Threshold ◽

Sphere Decoding ◽

High Signal ◽

Signal To Noise ◽

Multiple Input ◽

Input Multiple Output

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.

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Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization

Applied Sciences ◽

10.3390/app9101993 ◽

2019 ◽

Vol 9 (10) ◽

pp. 1993 ◽

Cited By ~ 2

Author(s):

Ende Wang ◽

Ping Jiang ◽

Xukui Hou ◽

Yalong Zhu ◽

Liangyu Peng

Keyword(s):

Wavelet Analysis ◽

Infrared Imaging ◽

Signal To Noise Ratio ◽

Vertical Component ◽

Infrared Image ◽

Structural Similarity ◽

Correction Algorithm ◽

Uncooled Infrared Imaging ◽

Correction Result ◽

Better Than

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

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A Fast Adaptive Beamforming Algorithm Based on Gram-Schmidt Orthogonalization

Journal of Nanoelectronics and Optoelectronics ◽

10.1166/jno.2021.3026 ◽

2021 ◽

Vol 16 (4) ◽

pp. 642-650

Author(s):

Bing-Feng Qian ◽

Shi-Jie Gao ◽

Quan-Feng Li ◽

Qian Zhang ◽

Ye Wang

Keyword(s):

Computational Complexity ◽

Covariance Matrix ◽

Signal To Noise Ratio ◽

Data Preprocessing ◽

Adaptive Threshold ◽

Adaptive Beamforming ◽

High Signal ◽

Signal To Noise ◽

Schmidt Orthogonalization ◽

Beamforming Algorithm

Fast implementation is one of the important indexes of the ADBF algorithm. The advantages of the Gram-Schmidt (GS) orthogonalization algorithm are that it can reconstruct the interference subspace well under the high signal-to-noise ratio and has fast convergence speed and low computational complexity. This paper studies the RGS algorithm for GS orthogonalization of sampling covariance matrix. To estimate the interference subspace more accurately, this paper modifies the orthogonal adaptive threshold of covariance matrix, and extends the proposed GS orthogonal algorithm of covariance matrix based on data preprocessing to the adaptive beamforming processing at subarray level.

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New Enhancement Algorithm of Infrared Images Based on Dual Threshold of the Exponential Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3893 ◽

2014 ◽

Vol 989-994 ◽

pp. 3893-3897

Author(s):

Jing Lv ◽

Jian Lv ◽

Chuan Fu Wu ◽

Yun Zhou ◽

Feng Pei Sun ◽

...

Keyword(s):

Visible Light ◽

Imaging System ◽

Median Filter ◽

Infrared Image ◽

Human Vision ◽

Infrared Images ◽

Threshold Method ◽

Exponential Transform ◽

Detail Image ◽

Enhancement Algorithm

Since the thermal imaging system detection ability and spatial resolution is lower than visible light CCD array,the definition of the infrared image is lower than that of the visible light image, which needs enhancement to improve image quality and for human vision. This paper proposes an enhancement algorithm of infrared images based on dual threshold of the exponential transform, firstly the original image can be processed by median filter to reduce noise,next separate the detail information of the 14-bit infrared image after noise reduction; then deal the detail image with the double threshold method based on the exponential transform,retaining or enhancing the detail information;when it comes to the base image,deal it with the gray linear mapping method, compressing its dynamic range.Finally the detail image and the base image are composed together after processing,getting 8-bit infrared image which can display and be processed on an ordinary display.

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Laser Beam Measurement Based on Image Enhancement Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.225-226.666 ◽

2011 ◽

Vol 225-226 ◽

pp. 666-669 ◽

Cited By ~ 3

Author(s):

Yu Hong Li ◽

Wei Ding ◽

Shan Ding

Keyword(s):

Laser Beam ◽

Mathematical Models ◽

Infrared Image ◽

Pso Algorithm ◽

Correction Algorithm ◽

Statistical Probability ◽

Low Contrast ◽

Invariant Moments ◽

Enhancement Algorithm ◽

Order Invariant

This paper puts forwards a novel algorithm for laser beam measurement based on the enhancement of infrared image, which has strong noise background and low contrast. First, laser beam image were divided by “three order invariant moments” with the idea of statistical probability, and objects that need to be measured were extracted; Then, four mathematical models were established on the basis of “γ-correcting” algorithm, and an improved“(a, b, γ)-correction” algorithm which is based on PSO algorithm has been proposed by solving the mathematical models respectively. The PSO algorithm will be used in the process of the enhancement of laser beam.

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Ultrasonic Imaging System Using Spatial and Temporal M-Sequences

Ultrasonic Imaging ◽

10.1177/016173467900100304 ◽

1979 ◽

Vol 1 (3) ◽

pp. 245-254 ◽

Cited By ~ 1

Author(s):

Takuso Sato ◽

Shusou Wadaka

Keyword(s):

Imaging System ◽

Signal To Noise Ratio ◽

Ultrasonic Imaging ◽

Spatial Modulation ◽

High Signal ◽

Temporal Modulation ◽

Range Resolution ◽

M Sequence ◽

Azimuth Resolution ◽

M Sequences

A new ultrasonic imaging system combining temporal modulation of the ultrasonic signal by an M-sequence with spatial modulation of the wave field by a rotating M-sequence phase disk has been developed. This system uses neither beam forming nor transducer scanning, and has high resolution both in range and azimuth. The correlation function of the temporal M-sequence determines the range resolution, while the azimuth resolution is determined by the configuration of the rotating M-sequence phase disk. The use of the M-sequence modulations also results in images with high signal-to-noise ratio.

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A New Method Based on HMMs and K-Means Algorithms for Noise-Robust Voice Activity Detector

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.128-129.461 ◽

2011 ◽

Vol 128-129 ◽

pp. 461-464 ◽

Cited By ~ 1

Author(s):

Bing Luo ◽

Zheng Pei ◽

Li Xu ◽

Da Li Hu

Keyword(s):

Markov Models ◽

Signal To Noise Ratio ◽

Adaptive Threshold ◽

High Signal ◽

Ratio Test ◽

Signal To Noise ◽

Voice Activity Detector ◽

Noise Rate ◽

Noise Robust ◽

Voice Activity

In this paper, we proposed left-right hidden Markov models (HMMs) combination with k-means threshold of Likelihood ratio test (LRT) to identify the start and end of the speech. This method builds two models of non-speech and speech but not two states, i.e. each model could conclude several states. In the experiments we present the Voice Activity Detection (VAD) results between two states hidden semi-Markov model (HSMM) and proposed algorithm. We also compare accuracy and robust between the k-means threshold and the adaptive threshold in high signal to noise rate in the background noise. It presents that k-means threshold is more effective than the adaptive threshold and the proposed method also make a better performance than two states HSMM based VAD, especially in the low signal-to-noise ratio (SNR) environment.

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