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Journal articles on the topic 'Thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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1

Huang, Hui Xian, Juan Gong, and Te Zhang. "Method of Adaptive Wavelet Thresholding Used in Image Denoising." Advanced Materials Research 204-210 (February 2011): 1184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1184.

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According to multi-resolution analysis of wavelet threshold denoising principle, this paper presented two improved algorithms of continuity and adaptive threshold based on hard thresholding. The soft thresholding (hyperbolic thresholding) was used in the intervals after setting two thresholds, and the isolated points were removed according to the adjacent correlation coefficient during the processing. As a result, the hard thresholding’s shortcomings were reduced. The simulation results show that improved algorithms have both better visual effect and PSNR than the traditional approaches.

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2

Inoue, A., K. Yamamoto, and N. Mizoue. "Comparison of automatic and interactive thresholding of hemispherical photography." Journal of Forest Science 57, No. 2 (February 18, 2011): 78–87. http://dx.doi.org/10.17221/68/2010-jfs.

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This study presents the effects of operator bias and variation in interactive thresholding on the estimation of light environment using hemispherical photography. Twenty-one hemispherical photographs taken beneath a wide range of canopy densities were visually converted to binary images twice by 21 operators, and then the gap fraction was computed from the images. The interactive threshold varied greatly among the different operators and within a single operator, which resulted in a considerable operator bias and variation in the gap fraction. This study also compared three widely used automatic thresholding algorithms, which were installed in freely available software LIA for Win32 for analyzing hemispherical photography, with interactive thresholding using the same photographs. The median of the interactive threshold by repetitive interactive thresholdings from 21 operators was assumed to be correct for the comparison. The results indicated that MINIMUM was considered to be a better algorithm than the other ones installed in LIA32 when the gap fraction was over 10%. However, VARIANCE seemed to be superior to MINIMUM under the low gap fraction and the cloudy sky condition with dark and white clouds. This implied that MINIMUM or VARIANCE should be used for analyzing hemispherical photographs with LIA32. In conclusion, we need to pay attention to the selection of the automatic thresholding algorithm and the sky condition when taking hemispherical photographs.

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3

Rosin, Paul L. "Unimodal thresholding." Pattern Recognition 34, no. 11 (November 2001): 2083–96. http://dx.doi.org/10.1016/s0031-3203(00)00136-9.

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4

Pal, Nikhil R., and Sankar K. Pal. "Entropic thresholding." Signal Processing 16, no. 2 (February 1989): 97–108. http://dx.doi.org/10.1016/0165-1684(89)90090-x.

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5

Kim, Min Hee, and Michael G. Akritas. "Order thresholding." Annals of Statistics 38, no. 4 (August 2010): 2314–50. http://dx.doi.org/10.1214/09-aos782.

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6

Aviad, Z., and E. Lozinskii. "Semantic thresholding." Pattern Recognition Letters 5, no. 5 (May 1987): 321–28. http://dx.doi.org/10.1016/0167-8655(87)90073-0.

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7

Liu, Haoyang, and Rina Foygel Barber. "Between hard and soft thresholding: optimal iterative thresholding algorithms." Information and Inference: A Journal of the IMA 9, no. 4 (December 10, 2019): 899–933. http://dx.doi.org/10.1093/imaiai/iaz027.

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Abstract Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell _q$ thresholding with appropriate choices of $q$ and a newly defined reciprocal thresholding operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.

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8

Dinc, Imren, Semih Dinc, Madhav Sigdel, Madhu S. Sigdel, Marc L. Pusey, and Ramazan S. Aygun. "Super-Thresholding: Supervised Thresholding of Protein Crystal Images." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 4 (July 1, 2017): 986–98. http://dx.doi.org/10.1109/tcbb.2016.2542811.

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9

Cui, Angang, Haizhen He, and Hong Yang. "A Designed Thresholding Operator for Low-Rank Matrix Completion." Mathematics 12, no. 7 (April 2, 2024): 1065. http://dx.doi.org/10.3390/math12071065.

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In this paper, a new thresholding operator, namely, designed thresholding operator, is designed to recover the low-rank matrices. With the change of parameter in designed thresholding operator, the designed thresholding operator can apply less bias to the larger singular values of a matrix compared with the classical soft thresholding operator. Based on the designed thresholding operator, an iterative thresholding algorithm for recovering the low-rank matrices is proposed. Numerical experiments on some image inpainting problems show that the proposed thresholding algorithm performs effectively in recovering the low-rank matrices.

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10

Desiani, Anita, Des Alwine Zayanti, Rifkie Primartha, Filda Efriliyanti, and Nur Avisa Calista Andriani. "Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 2 (August 21, 2021): 255. http://dx.doi.org/10.26418/jp.v7i2.47205.

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Segmentasi pembuluh darah pada retina diperlukan pada deteksi dini penyakit Diabetic Retinopathy pada citra retina. Penelitian ini menggunakan tiga tahapan yaitu pre-processing, segmentasi dan post-processing yang akan membandingkan hasil dari 3 metode segmentasi yang menggunakan nilai Thresholding yaitu Adaptive Thresholding, Binary Thresholding, dan Otsu Thresholding. Hasil pengujian terhadap tiga metode yang digunakan menunjukan bahwa metode Binary Thresholding mendapat rata-rata akurasi, sensitivitas dan spesifisitas tertinggi yaitu 95%, 58%, 98%. Untuk Adaptive Thresholding mendapat rata-rata akurasi sebesar 91%, sensitivitas 36%, spesititiftas 97%. Dan metode Otsu Thresholding mendapatkan rata-rata akurasi 86%, sensitivitas 22%, dan spesifisitas 90%. Dari hasil ketiga metode ini dapat dilihat akurasi yang dihasilkan oleh metode Thresholding sudah sangat baik dalam melakukan segmentasi citra, tetapi nilai sensitivitas dari masing-masing metode Thresholding masih rendah. Hal ini dapat disimpulkan metode Thresholding masih sulit mendapatkan lebih banyak fitur pembuluh darah pada citra retina.

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11

Yin, Peng-Yeng, and Ling-Hwei Chen. "Random-sampling thresholding: A new approach to multilevel thresholding." Signal Processing 34, no. 3 (December 1993): 311–22. http://dx.doi.org/10.1016/0165-1684(93)90138-z.

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12

Kerkyacharian, Gérard, Erwan Le Pennec, and Dominique Picard. "Radon needlet thresholding." Bernoulli 18, no. 2 (May 2012): 391–433. http://dx.doi.org/10.3150/10-bej340.

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13

Yu-Kun Lai and Paul L. Rosin. "Efficient Circular Thresholding." IEEE Transactions on Image Processing 23, no. 3 (March 2014): 992–1001. http://dx.doi.org/10.1109/tip.2013.2297014.

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14

Jain, Prateek, Ambuj Tewari, and Inderjit S. Dhillon. "Partial Hard Thresholding." IEEE Transactions on Information Theory 63, no. 5 (May 2017): 3029–38. http://dx.doi.org/10.1109/tit.2017.2686880.

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15

Sun, Qiang, Bai Jiang, Hongtu Zhu, and Joseph G. Ibrahim. "Hard thresholding regression." Scandinavian Journal of Statistics 46, no. 1 (September 24, 2018): 314–28. http://dx.doi.org/10.1111/sjos.12353.

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16

Kittler, J., and J. Illingworth. "Minimum error thresholding." Pattern Recognition 19, no. 1 (January 1986): 41–47. http://dx.doi.org/10.1016/0031-3203(86)90030-0.

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17

Olhede, S. C. "'Analytic' wavelet thresholding." Biometrika 91, no. 4 (December 1, 2004): 955–73. http://dx.doi.org/10.1093/biomet/91.4.955.

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18

Cosentino, Romain, Randall Balestriero, Richard G. Baraniuk, and Behnaam Aazhang. "Universal Frame Thresholding." IEEE Signal Processing Letters 27 (2020): 1115–19. http://dx.doi.org/10.1109/lsp.2020.3001457.

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19

Katsuragawa, Keiko, Ankit Kamal, Qi Feng Liu, Matei Negulescu, and Edward Lank. "Bi-Level Thresholding." ACM Transactions on Interactive Intelligent Systems 9, no. 2-3 (April 25, 2019): 1–30. http://dx.doi.org/10.1145/3181672.

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20

Zou, Yaobin, Fangmin Dong, Bangjun Lei, Shuifa Sun, Tingyao Jiang, and Peng Chen. "Maximum similarity thresholding." Digital Signal Processing 28 (May 2014): 120–35. http://dx.doi.org/10.1016/j.dsp.2014.02.008.

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21

Fornasier, Massimo, and Holger Rauhut. "Iterative thresholding algorithms." Applied and Computational Harmonic Analysis 25, no. 2 (September 2008): 187–208. http://dx.doi.org/10.1016/j.acha.2007.10.005.

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22

Fang, Ning, P. Pai Srinivasa, and Nathan Edwards. "Wavelet-Based Denoising of Vibration Signals for Tool-Edge Wear Detection in High Speed Machining of Inconel 718." Advanced Materials Research 415-417 (December 2011): 1512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.415-417.1512.

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Denoising is an essential step and plays a significant role in tool condition monitoring. In the present study, four wavelet-based denoising techniques are studied and compared, including conventional hard-thresholding, conventional soft-thresholding, generalized soft-thresholding, and soft-thresholding with Stein’s unbiased risk estimate (SURE). The results show that soft-thresholding with SURE generates the lowest mean squared error, and hence is the most appropriate denoising technique for tool-edge wear detection in high speed machining of Inconel 718.

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23

Yang, Guifeng, Jiulun Fan, and Dong Wang. "Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram." Mathematical Problems in Engineering 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653031.

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Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

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24

Karakoyun, Murat, Nurdan Akhan Baykan, and Mehmet Hacibeyoglu. "Multi-Level Thresholding for Image Segmentation With Swarm Optimization Algorithms." International Research Journal of Electronics and Computer Engineering 3, no. 3 (September 28, 2017): 1. http://dx.doi.org/10.24178/irjece.2017.3.3.01.

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Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That’s why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-level thresholding technique uses n threshold values where n greater than one. In this paper, two swarm optimization algorithms (Particle Swarm Optimization, PSO and Cat Swarm Optimization, CSO) are applied on finding the optimum threshold values for the multi-level thresholding. In literature, there are some minimization or maximization functions to find the best threshold values for thresholding problem. Some of these methods are: Tsalli’s Entropy, Kapur’s Entropy, Renyi’s Entropy, Otsu’s Method (within class variance/between class variance), the Minimum Cross Entropy Thresholding (MCET) etc.In this work, Otsu’s (within class variance) method, which is one of these popular functions,is used as the fitness function of algorithms.In the experiments, five real images are segmented by usingParticle Swarm Algorithm and Cat Swarm Optimization Algorithms. The performances of the swarm algorithms on multi-level thresholding problem arecompared with Peak Signal-to-Noise Ratio (PSNR) and fitness function (FS) values. As a result, the PSO yields better performance than CSO.

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25

Sadatsharifi, Kasra, Mohamed A. Naiel, Mark Lamm, and Paul Fieguth. "Locally Adaptive Thresholding for Single-Shot Structured Light Patterns." Journal of Computational Vision and Imaging Systems 6, no. 1 (January 15, 2021): 1–3. http://dx.doi.org/10.15353/jcvis.v6i1.3556.

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Image thresholding is a challenging task due to its sensitivity to environmental variations and degradation in the quality of the captured image. Although many image thresholding methods have been introduced, most of them require the fine tuning of a thresholding parameter that is not suitable for single-shot structured light (SSSL) based projector-camera applications. In this paper, we introduce a locally adaptive thresholding method with automatic parameter selection based on the local statistics of the distinct image partitions. For assessing the proposed scheme, we introduce an evaluation that relies on mapping SSSL patterns between the camera and projector spaces. Experimental results demonstrate the effectiveness of the proposed technique by maintaining the thresholding accuracy of the baseline method, without the need to fine tune the obtained thresholding parameter or any noticeable change in the qualitative results.

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26

Yu, Yuncai, Xinsheng Liu, Ling Liu, and Weisi Liu. "On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise." Mathematica Slovaca 69, no. 6 (December 18, 2019): 1485–500. http://dx.doi.org/10.1515/ms-2017-0324.

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Abstract This paper considers the nonparametric regression model with negatively super-additive dependent (NSD) noise and investigates the convergence rates of thresholding estimators. It is shown that the term-by-term thresholding estimator achieves nearly optimal and the block thresholding estimator attains optimal (or nearly optimal) convergence rates over Besov spaces. Additionally, some numerical simulations are implemented to substantiate the validity and adaptivity of the thresholding estimators with the presence of NSD noise.

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27

Al-Bayati, Moumena, and Ali El-Zaart. "Mammogram Images Thresholding for Breast Cancer Detection Using Different Thresholding Methods." Advances in Breast Cancer Research 02, no. 03 (2013): 72–77. http://dx.doi.org/10.4236/abcr.2013.23013.

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28

bonga, Siya, and Shi ra. "Separation from Brain Magnetic Resonance images (MRI) using Multistage Thresholding Technique." International Journal of Pharmacy and Biomedical Engineering 2, no. 3 (December 25, 2015): 9–12. http://dx.doi.org/10.14445/23942576/ijpbe-v2i3p103.

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Image separation is a significant task concerned in dissimilar areas from image dispensation to picture examination. One of the simplest methods for image segmentation is thresholding. Though, many thresholding methods are based on a bi-level thresholding process. These methods can be extensive to form multi-level thresholding, but they become computationally expensive since a large number of iterations would be necessary for computing the most select threshold values. In order to conquer this difficulty, a new process based on a Shrinking Search Space (3S) algorithm is proposed in this paper. The method is applied on statistical bi-level thresholding approaches including Entropy, Cross-entropy, Covariance, and Divergent Based Thresholding (DBT), to attain multi-level thresholding and used for separation from brain MRI images. The paper demonstrates that the collision of the proposed 3S method on the DBT method is more important than the other bi-level thresholding approaches. Comparing the results of using the proposed approach against those of the Fuzzy C-Means (FCM) clustering method demonstrates a better segmentation performance by improving the comparison index from 0.58 in FCM to 0.68 in the 3S method. Also, this method has a lower calculation impediment of around 0.37s with admiration to 157s dispensation time in FCM. In addition, the FCM approach does not always guarantee the convergence, whilst the 3S method always converges to the optimal result.

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29

Kryjak, Tomasz, and Marek Gorgoń. "Parallel implementation of local thresholding in Mitrion-C." International Journal of Applied Mathematics and Computer Science 20, no. 3 (September 1, 2010): 571–80. http://dx.doi.org/10.2478/v10006-010-0042-2.

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Parallel implementation of local thresholding in Mitrion-CMitrion-C based implementations of three image processing algorithms: a look-up table operation, simple local thresholding and Sauvola's local thresholding are described. Implementation results, performance of the design and FPGA logic utilization are discussed.

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30

Wang, Yanmei, Mingyu Lu, and Pengfei Feng. "Research on Road Extraction Algorithm Based on Improved Otsu’s Thresholding Method." Journal of Physics: Conference Series 2364, no. 1 (November 1, 2022): 012064. http://dx.doi.org/10.1088/1742-6596/2364/1/012064.

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Abstract In this paper, the widely used method of Otsu's thresholding method is studied. On the basis of the traditional Otsu's thresholding method, according to the distribution characteristics of the target and background in the actual image, a new method based on traditional Otsu's thresholding method is presented.and the road extraction experiment is carried out with GF-2 remote sensing image.The experimental results show that the improved method has the advantages of high segmentation accuracy and strong anti noise ability compared with the traditional Otsu's thresholding method.

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31

Janariah, Muhammad Luthfie, Syamsul Bahri, and Nurul Fitriyani. "PENERAPAN METODE WAVELET THRESHOLDING UNTUK MENGAPROKSIMASI FUNGSI NONLINIER." Indonesian Physical Review 4, no. 3 (August 16, 2021): 122–37. http://dx.doi.org/10.29303/ipr.v4i3.98.

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The wavelet thresholding method is an approximation method by reducing noise, which is known as the denoising process. This denoising process will remove noise while closed the important information in the data. In this research, the wavelet thresholding method is used to approximate the nonlinear function. The data used for the simulation is a representation of several functions that represent several events that often occur in the real world, which consists of the types of functions Blocks, Bumps, Doppler, and HeaviSine. Based on simulation results based on the indicator value of the Cross-Validation (CV), the best approximation of the nonlinear function using the wavelet thresholding method for the four simulation cases are: (i) the Blocks function is given by Haar wavelet with a soft of thresholding function and the 10-th resolution level ; (ii) the Doppler function is given on the 2-nd order of Symlets wavelet with a soft of thresholding function and the 10-th resolution level; (iii) the Bumps function is given on the 6-th order of Daubechies wavelet with a soft of thresholding function and the 10-th resolution level; and (iv) the HeaviSine function is given by the 3-rd order of Coiflet wavelet with a soft of thresholding function and the 7-th resolution level.

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation." International Journal of Applied Metaheuristic Computing 8, no. 4 (October 2017): 58–83. http://dx.doi.org/10.4018/ijamc.2017100104.

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Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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33

Roshan, A., and Y. Zhang. "MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-173-2019.

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<p><strong>Abstract.</strong> Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed a background subtraction technique using Lab colour space and used spatial correlations for image thresholding. Four thresholding methods using spatial correlation are developed by computing the difference between opposite colour pairs of background and foreground frames. Out of 9 indoor and outdoor scenes, the object is detected successfully in 7 scenes whereas existing background subtraction technique using grey images with commonly used thresholding methods detected moving objects in 1–5 scenes. Shape and boundaries of detected objects are also better defined using the developed technique.</p>

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Li, Kun, Na Yang, Jian Wang, Yan Han, Peng-Fei Nie, and Min Zhang. "Size Projection Algorithm: Optimal Thresholding Value Selection for Image Segmentation of Electrical Impedance Tomography." Mathematical Problems in Engineering 2019 (October 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/1368010.

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Thresholding is an efficient step to extract quantitative information since the potential artefacts are often introduced by the point-spread effect of tomographic imaging. The thresholding value was previously selected only relying on engineering experience or histogram of tomographic image, which often presents a great challenge to determine an accurate thresholding value for various applications. As the tomographic image features often do not provide sufficient information to choose the best thresholding value, the information implicit in the measured boundary data is introduced into the thresholding process in this paper. A projection error, the relative difference between the computed boundary data of current segmentation and the measured boundary data, is proposed as a quantitative measure of such image segmentation quality. Then, a new multistep image segmentation process, called size projection algorithm (SPA), is proposed to automatically determine an optimal thresholding value by minimising the projection error. Results of simulation and experiment demonstrate the improved performance of the SPA-based tomographic image segmentation. An application of size projection algorithm for gas-water two-phase flow visualisation is also reported in this paper.

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Pekárková, Kristýna, Jakub Soukup, Marie Kostelanská, Jan Širc, Zbyněk Straňák, and Karel Holada. "Cord Blood Extracellular Vesicles Analyzed by Flow Cytometry with Thresholding Using 405 nm or 488 nm Laser Leads to Concurrent Results." Diagnostics 11, no. 8 (July 22, 2021): 1320. http://dx.doi.org/10.3390/diagnostics11081320.

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Extracellular vesicles (EVs) from liquid biopsies are extensively analyzed by flow cytometry, a technology that is continuously evolving. Thresholding utilizing a violet 405 nm laser side scatter (VSSC) has recently been implemented. Here, we collected set of large EV (lEV) samples from cord blood, which we analyzed using a standard flow cytometer improved via a 405 nm laser side scatter. Samples were analyzed using two distinct thresholding methods—one based on VSSC, and one based on VSSC combined with fluorescence thresholding on stained phosphatidylserine. Through these thresholding methods, we compared lEVs from pre-term births and control cord blood. Double-labeled lEVs with platelet CD36+/CD41+, activated platelet CD41+/CD62P+ and endothelial CD31+/CD105+ antibodies were used. Apart from comparing the two groups together, we also correlated measured lEVs with the thresholding methods. We also correlated the results of this study with data analyzed in our previous study in which we used a conventional 488 nm laser SSC. We did not find any difference between the two cord blood groups. However, we found highly concurrent data via our correlation of the thresholding methods, with correlation coefficients ranging from 0.80 to 0.96 even though the numbers of detected lEVs differed between thresholding methods. In conclusion, our approaches to thresholding provided concurrent data and it seems that improving the cytometer with the use of a VSSC increases its sensitivity, despite not being particularly critical to the validity of flow cytometric studies that compare pathological and physiological conditions in liquid biopsies.

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36

Chikanbanjar, Milan. "Comparative analysis between non-linear wavelet based image denoising techniques." Journal of Science and Engineering 5 (August 31, 2018): 58–67. http://dx.doi.org/10.3126/jsce.v5i0.22373.

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Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) and SSIM (Structural Similarity index). Here, non-linear wavelet transform denoising techniques of natural images are studied, analyzed and compared using thresholding techniques such as soft, hard, semi-soft, LevelShrink, SUREShrink, VisuShrink and BayesShrink. On most of the tests, PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods. For instance, from tests PSNR and SSIM values of lena image for VISUShrink Hard, VISUShrink Soft, VISUShrink Semi Soft, LevelShrink Hard, LevelShrink Soft, LevelShrink Semi Soft, SUREShrink, BayesShrink thresholding methods at the variance of 10 are 23.82, 16.51, 23.25, 24.48, 23.25, 20.67, 23.42, 23.14 and 0.28, 0.28, 0.28, 0.29, 0.22, 0.25, 0.16 respectively which shows that the PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods, and so on. Thus, it can be stated that the performance of LevelShrink Hard thresholding method is better on most of tests.

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37

Xing, Xue Min. "Design of PS Identification Algorithm in Ground Deformation Monitoring." Advanced Materials Research 933 (May 2014): 235–38. http://dx.doi.org/10.4028/www.scientific.net/amr.933.235.

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In this paper, an algorithm of combining the Coherence Factor Thresholding (CFT) method, Amplitude Deviation Thresholding (ADT) method and Intensity Index Thresholding (IIT) method ( Triple Thresholding: TT algorithm) is designed, which can be applied in the identification of PS points in the PSInSAR algorithm to monitor the ground deformation, On the basis of analyzing and comparing the deficiency of each identification method, the detail flow of the TT algorithm has been designed, which will perform more suitable to monitor the ground deformation theoretically. The real data experiment using the algorithm will be carried out in further work.

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Liu, Shou Shan, Chuan Jiang Wang, Li Jun Bi, and Chang Zhi Lv. "Application of Adaptive Wavelet Thresholding to Ultrasonic Signal Compression of Aluminum Alloy Forgings." Advanced Materials Research 658 (January 2013): 89–92. http://dx.doi.org/10.4028/www.scientific.net/amr.658.89.

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In this paper, for the purpose of ultrasonic signal compression and the coherent noise depressing in nondestructive test of aluminum alloy forging, the mathematical model of defect echoes is discussed and confirmed. And then the wavelet kernel is also confirmed according the waveform of the defect echoes. As the algorithms of standard hard thresholding and soft thresholding of wavelet transform can not bring out effective compression and depression to the coherent noise, an adaptive wavelet thresholding algorithm is introduced. Experimental results indicate that the adaptive wavelet thresholding algorithm can offer effective signal compression and depression to the coherent noise.

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CHEN, GUANGYI, TIEN D. BUI, and ADAM KRZYZAK. "DENOISING OF THREE-DIMENSIONAL DATA CUBE USING BIVARIATE WAVELET SHRINKING." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 03 (May 2011): 403–13. http://dx.doi.org/10.1142/s0218001411008725.

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The denoising of a natural signal/image corrupted by Gaussian white noise is a classical problem in signal/image processing. However, it is still in its infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick's bivariate wavelet thresholding from two-dimensional (2D) image denoising to three-dimensional (3D) data cube denoising. Our study shows that bivariate wavelet thresholding is still valid for 3D data cubes. Experimental results show that bivariate wavelet thresholding on 3D data cube is better than performing 2D bivariate wavelet thresholding on every spectral band separately, VisuShrink, and Chen and Zhu's 3-scale denoising.

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Li, Yangyang, Lin Kong, Fanhua Shang, Yuanyuan Liu, Hongying Liu, and Zhouchen Lin. "Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8501–9. http://dx.doi.org/10.1609/aaai.v35i10.17032.

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Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. Moreover, most LISTA methods use the soft thresholding function, which has been found to cause a large estimation bias. Therefore, we propose a thresholding function for ELISTA instead of soft thresholding. From a theoretical perspective, we prove that our method attains linear convergence. Through ablation experiments, the improvements of our method on the network structure and the thresholding function are verified in practice. Extensive empirical results verify the advantages of our method.

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Kumaseh, Max R., Luther Latumakulita, and Nelson Nainggolan. "SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDING." JURNAL ILMIAH SAINS 13, no. 1 (May 17, 2013): 74. http://dx.doi.org/10.35799/jis.13.1.2013.2057.

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SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDINGABSTRAK Untuk mengenal jenis-jenis ikan berdasarkan ciri-cirinya, telah dibuat suatu sistem untuk memisahkan objek mata ikan menggunakan metode thresholding. Prosesnya dimulai dengan menginput citra digital ikan, selanjutnya dikonversi ke citra grayscale. Kemudian dilakukan proses segmentasi terhadap citra grayscale. Selanjutnya, dipilih hasil segmentasi dan ditandai dengan proses deteksi tepi menggunakan operator Canny yang dipertajam dengan proses dilasi. Proses terakhir adalah membuat plot contour terhadap hasil proses dilasi dan citra grayscale. Hasil segmentasi berhasil memisahkan objek mata ikan dengan menggunakan metode thresholding local. Keseluruhan proses ini dilakukan dengan menggunakan MATLAB R2012a. Kata kunci : Mata Ikan, Segmentasi Citra, Thresholding DIGITAL FISH IMAGE SEGMENTATION BY THRESHOLDING METHOD ABSTRACT A system of fish eyelets seperation has been conducted to identify types of fish acording to their characteristics, by using thresholding method. The process start by inserting digital fish image then convert it to grayscale image. Next step is to process segmentation the grayscale image. Choosed the segmentation result then marked it by edge detection process using Canny operation which has been sharpened by dilation process. The last step is to make contour plot to dilation result and grayscale image. The result of the segmentation shows that the fish eyelets can be separated using local thresholding method. The whole process is conducted by using MATLAB R2012a. Keywords : Fish Eyelets, Segmentation Image, Thresholding

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Kallummil, Sreejith, and Sheetal Kalyani. "Generalized residual ratio thresholding." Signal Processing 197 (August 2022): 108531. http://dx.doi.org/10.1016/j.sigpro.2022.108531.

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Arines, J., and J. Ares. "Minimum variance centroid thresholding." Optics Letters 27, no. 7 (April 1, 2002): 497. http://dx.doi.org/10.1364/ol.27.000497.

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Hassan, M. H., and P. Siy. "Real-time thresholding technique." Electronics Letters 23, no. 7 (1987): 339. http://dx.doi.org/10.1049/el:19870251.

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Font-Clos, Francesc, Gunnar Pruessner, Nicholas R. Moloney, and Anna Deluca. "The perils of thresholding." New Journal of Physics 17, no. 4 (April 30, 2015): 043066. http://dx.doi.org/10.1088/1367-2630/17/4/043066.

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Hou, Z., Q. Hu, and W. L. Nowinski. "On minimum variance thresholding." Pattern Recognition Letters 27, no. 14 (October 2006): 1732–43. http://dx.doi.org/10.1016/j.patrec.2006.04.012.

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Li, Zuoyong, Jian Yang, Guanghai Liu, Yong Cheng, and Chuancai Liu. "Unsupervised range-constrained thresholding." Pattern Recognition Letters 32, no. 2 (January 2011): 392–402. http://dx.doi.org/10.1016/j.patrec.2010.09.020.

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Obidin, M. V., and A. P. Serebrovski. "Wavelets and adaptive thresholding." Journal of Communications Technology and Electronics 59, no. 12 (December 2014): 1434–39. http://dx.doi.org/10.1134/s106422691412016x.

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Blumensath, Thomas. "Accelerated iterative hard thresholding." Signal Processing 92, no. 3 (March 2012): 752–56. http://dx.doi.org/10.1016/j.sigpro.2011.09.017.

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Yazid, Haniza, and Hamzah Arof. "Gradient based adaptive thresholding." Journal of Visual Communication and Image Representation 24, no. 7 (October 2013): 926–36. http://dx.doi.org/10.1016/j.jvcir.2013.06.001.

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