scholarly journals Infrared Dim Target Detection Using Shearlet’s Kurtosis Maximization under Non-Uniform Background

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 723 ◽  
Author(s):  
Lingbing Peng ◽  
Tianfang Zhang ◽  
Yuhan Liu ◽  
Meihui Li ◽  
Zhenming Peng
Keyword(s):  
Target Detection ◽  
High Frequency ◽  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Translation Invariance ◽  
Shearlet Transform ◽  
Fused Image ◽  
Uniform Background ◽  
Novel Method ◽  
Kurtosis Maximization

A novel method based on multiscale and multidirectional feature fusion in the shearlet transform domain and kurtosis maximization for detecting the dim target in infrared images with a low signal-to-noise ratio (SNR) and serious interference caused by a cluttered and non-uniform background is presented in this paper. First, an original image is decomposed using the shearlet transform with translation invariance. Second, various directions of high-frequency subbands are fused and the corresponding kurtosis of fused image is computed. The targets can be enhanced by strengthening the column with maximum kurtosis. Then, processed high-frequency subbands on different scales of images are merged. Finally, the dim targets are detected by an adaptive threshold with a maximum contrast criterion (MCC). The experimental results show that the proposed method has good performance for infrared target detection in comparison with the nonsubsampled contourlet transform (NSCT) method.

Image Fusion Based on Shearlet and Multi-Decision

2014 ◽  
Vol 889-890 ◽  
pp. 1103-1106
Author(s):  
Xin Zheng ◽  
Ai Ping Cai
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Fusion ◽  
High Frequency ◽  
Decision Rules ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Traditional Image

Image Fusion is an important and useful subject in Image Processing and Computer Vision. The traditional image fusion algorithm could not provide satisfactory fusion results. Aiming to solving this problem, in this paper, we proposed an algorithm based on shearlet and multi-decision. First we discussed the application of the shearlet transform. Then we use difference decision rules for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach can provide more satisfactory fusion outcome.

An Image Fusion Method Based on Shearlet and Multi-Decision

2014 ◽  
Vol 530-531 ◽  
pp. 390-393
Author(s):  
Yong Wang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Focus Image ◽  
Image Detail ◽  
Image Fusion Method ◽  
Traditional Image

Image processing is the basis of computer vision. Aiming at some problems existed in the traditional image fusion algorithm, a novel algorithm based on shearlet and multi-decision is proposed. At first we discussed multi-focus image fusion and then we use Shearlet transform and multi-decision for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach retains image detail and more clarity.

A Novel Fusion Algorithm of Visible Image and Infrared Image Based on NSCT

2012 ◽  
Vol 424-425 ◽  
pp. 223-226 ◽  
Author(s):  
Zheng Hong Cao ◽  
Yu Dong Guan ◽  
Peng Wang ◽  
Chun Li Ti
Keyword(s):  
High Frequency ◽  
Infrared Image ◽  
Low Frequency ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Visible Image ◽  
Fusion Rules ◽  
Fused Image ◽  
Novel Method

This paper focuses on the fusion method of visible image and infrared image, making in-depth discussion on the existing algorithms and proposes a novel method on the fusion rules. The image is firstly decomposed into low-frequency and high-frequency coefficients by NSCT and the characteristics of visible image and infrared image are then taken into account to finish the fusion. Finally, the quality of the fused image by different algorithms is compared with several existing criterions. MATLAB is employed to finish the simulation and the results will demonstrate this algorithm can improve the quality of the fused image effectively and the features in the image won’t be missing

A Novel Method of High Frequency Weak Signal Detection Based on Chaotic Oscillator System and Wavelet Transform System

2011 ◽  
Vol 130-134 ◽  
pp. 2770-2773
Author(s):  
Shuo Shi ◽  
Jin Yan Li ◽  
Xue Mai Gu
Keyword(s):  
Wavelet Transform ◽  
Signal Detection ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Chaotic Oscillator ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Signal To Noise ◽  
Weak Signals ◽  
Novel Method

Based on chaotic oscillator system and wavelet transform system, this paper proposes a novel method on high frequency weak signal detection. Chaotic system is a typical non-linear system which is sensitive to certain signals and immune to noise at the same time. Its properties demonstrate the potential application on weak signal detection. Due to the good localization in both time domain and frequency domain, the wavelet transform method can automatically adjust to different frequency components and increase the Signal-to-Noise Ratio. Starting from the analysis of advantages and disadvantages of two signal detection methods, we put forward a combined method that takes advantage of each method to detect weak signals with high frequency. The simulation results show that the novel method can detect weak signals with frequency in an order of magnitude of 107Hz, and the input Signal-to-Noise Ratio threshold could be-42.5dB.

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

scholarly journals Development of the Persian version of high-frequency emphasis quick speech in noise

2019 ◽  
Author(s):  
Jamileh Fatahi ◽  
Maryam Amiri Jahromi ◽  
Fahimeh Hajiabolhassan ◽  
Amirsalar Jafarpisheh ◽  
Nariman Rahbar ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Mean Value ◽  
Small Sample ◽  
Test Reliability ◽  
Face Validity ◽  
Mean Values ◽  
Speech In Noise ◽  
Persian Version ◽  
The Mean

Background and Aim: The quick speech in noise (Q-SIN) test shows the difficulty of spee­ch perception in noise by specifying signal to noise ratio (SNR) loss. Although the Persian version of Q-SIN has been already constructed, the high-frequency emphasis version of this test is not available. The present study aimed to construct six lists with high-frequency emphasis and implement it. Methods: We are going to prepare a high-frequ­ency emphasis version of Q-SIN and then test it on a small sample. First, researchers designed the relevant sentences; then experts examined their content and face validity. According to the criteria for developing the Q-SIN test, six lists with high-frequency emphasis were prepared. The test was examined on 26 (13 male and 13 female), 18−35 years old individuals with nor­mal hearing. To determine the test reliability, it was re-administered three weeks later with the same conditions. Results: Of 76 sentences prepared, 36 sentences received enough credit after determination of their content and face validity. These 36 senten­ces were used to make 6 lists. The mean value of SNR50 in the Persian language was obtained -4 dB. The mean values of SNR loss in 6 lists were -1.65, -1.8, -2.23, -1.61, -2.38 and -2.07. The results showed equivalency of lists 1, 2, 3, 4, and 6. Examination of test-retest reliability indicated that all lists except the list 2were reliable. Conclusion: The lists of 1, 3, 4, and 6 are reli­able and equivalent and can be used in clinical application.

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