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Статті в журналах з теми "Smoothing image"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

BASU, MITRA, and MIN SU. "IMAGE SMOOTHING WITH EXPONENTIAL FUNCTIONS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (June 2001): 735–52. http://dx.doi.org/10.1142/s0218001401001076.

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Анотація:

Noise reduction in images, also known as image smoothing, is an essential and first step before further processings of the image. The key to image smoothing is to preserve important features while removing noise from the image. Gaussian function is widely used in image smoothing. Recently it has been reported that exponential functions (value of the exponent is not equal to 2) perform substantially better than Gaussian functions in modeling and preserving image features. In this paper we propose a family of exponential functions, that include Gaussian when the value of the exponent is 2, for image smoothing. We experiment with a variety of images, artificial and real, and demonstrate that optimal results are obtained when the value of the exponent is within a certain range.

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2

Sirur, Kedir Kamu, Ye Peng, and Qinchuan Zhang. "Smoothing Filters for Waveform Image Segmentation." International Journal of Machine Learning and Computing 7, no. 5 (October 2017): 139–43. http://dx.doi.org/10.18178/ijmlc.2017.7.5.636.

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3

Pizarro, Luis, Pavel Mrázek, Stephan Didas, Sven Grewenig, and Joachim Weickert. "Generalised Nonlocal Image Smoothing." International Journal of Computer Vision 90, no. 1 (April 9, 2010): 62–87. http://dx.doi.org/10.1007/s11263-010-0337-7.

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4

Meer, P., R. H. Park, and K. J. Cho. "Multiresolution Adaptive Image Smoothing." CVGIP: Graphical Models and Image Processing 56, no. 2 (March 1994): 140–48. http://dx.doi.org/10.1006/cgip.1994.1013.

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5

Xu, Hui-hong, and Dong-yuan Ge. "A novel image edge smoothing method based on convolutional neural network." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092167. http://dx.doi.org/10.1177/1729881420921676.

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In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

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6

Peng, Anjie, Gao Yu, Yadong Wu, Qiong Zhang, and Xiangui Kang. "A Universal Image Forensics of Smoothing Filtering." International Journal of Digital Crime and Forensics 11, no. 1 (January 2019): 18–28. http://dx.doi.org/10.4018/ijdcf.2019010102.

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Digital image smoothing filtering operations, including the average filtering, Gaussian filtering and median filtering are always used to beautify the forged images. The detection of these smoothing operations is important in the image forensics field. In this article, the authors propose a universal detection algorithm which can simultaneously detect the average filtering, Gaussian low-pass filtering and median filtering. Firstly, the high-frequency residuals are used as being the feature extraction domain, and then the feature extraction is established on the local binary pattern (LBP) and the autoregressive model (AR). For the LBP model, the authors exploit that both of the relationships between the central pixel and its neighboring pixels and the relationships among the neighboring pixels are differentiated for the original images and smoothing filtered images. A method is further developed to reduce the high dimensionality of LBP-based features. Experimental results show that the proposed detector is effective in the smoothing forensics, and achieves better performance than the previous works, especially on the JPEG images.

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7

Et. al., Ch Kavya ,. "Performance Analysis of Different Filters for Digital Image Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2572–76. http://dx.doi.org/10.17762/turcomat.v12i2.2220.

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Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concerning digital image processing. Here linear, as well as non-linear filters, are presented. It is easy to decide about the better filter for improving the image processing output from the discussion.

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8

HE, QINBIN, and FANGYUE CHEN. "DESIGNING CNN GENES FOR BINARY IMAGE EDGE SMOOTHING AND NOISE REMOVING." International Journal of Bifurcation and Chaos 16, no. 10 (October 2006): 3007–13. http://dx.doi.org/10.1142/s0218127406016604.

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Edge smoothing and noise removing for images are a common method of image processing. By designing CNN genes, edges can be smoothed and particles can be removed from a binary image. However, a satisfying result cannot be obtained by choosing only one CNN gene. In this paper, a group of edge smoothing and noise removing CNN genes is proposed as a synthetic disposal for a binary image. Disposed by the group of CNN genes, the characteristics of the original image can be preserved as much as possible. Two examples of edge smoothing and noise removing for a binary image are well illustrated by this method in this paper.

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9

Ma, Xiang, Xuemei Li, Yuanfeng Zhou, and Caiming Zhang. "Image smoothing based on global sparsity decomposition and a variable parameter." Computational Visual Media 7, no. 4 (May 17, 2021): 483–97. http://dx.doi.org/10.1007/s41095-021-0220-1.

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AbstractSmoothing images, especially with rich texture, is an important problem in computer vision. Obtaining an ideal result is difficult due to complexity, irregularity, and anisotropicity of the texture. Besides, some properties are shared by the texture and the structure in an image. It is a hard compromise to retain structure and simultaneously remove texture. To create an ideal algorithm for image smoothing, we face three problems. For images with rich textures, the smoothing effect should be enhanced. We should overcome inconsistency of smoothing results in different parts of the image. It is necessary to create a method to evaluate the smoothing effect. We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems. A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter. Three evaluation measures: edge integrity rate, texture removal rate, and gradient value distribution are proposed to cope with the third problem. We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results. Experiments show that our algorithm is better than existing algorithms both visually and quantitatively. We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.

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10

Zhang, Xiaohua, Yuelan Xin, and Ning Xie. "Anisotropic Joint Trilateral Rolling Filter for Image Smoothing." Journal of the Institute of Industrial Applications Engineers 7, no. 3 (July 25, 2019): 91–98. http://dx.doi.org/10.12792/jiiae.7.91.

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11

Qiu, Peihua, and Jingran Sun. "Local Smoothing Image Segmentation for Spotted Microarray Images." Journal of the American Statistical Association 102, no. 480 (December 2007): 1129–44. http://dx.doi.org/10.1198/016214506000001158.

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12

Sharma, Vijay, Devesh Srivastava, and Pratistha Mathur. "A Daubechies DWT Based Image Steganography Using Smoothing Operation." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 154–61. http://dx.doi.org/10.34028/iajit/17/2/2.

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Steganography is a capability which conceals the top-secret information into cover media (e.g., digital images, sound files etc.,). This Paper presents a secure, higher embedding capacity Discrete Wavelet Transformation (DWT) based technique. Before embedding correlation in between cover and the secret image is increased by multiplying some variable (i.e., 1/k) to the secret image. In embedding process, the Daubechies DWT of both Arnold transformed secret and cover images are taken followed by alpha blending operation. Arnold is a type of scrambling process which increases the confidentiality of secret image and alpha blending is a type of mixing operation of two images, the alpha value indicates the amount of secret image is embedded into the cover image. Daubechies Inverse Discrete Wavelet Transformation (IDWT) of the resulting image is performed to obtain the stego image. Smoothing operation inspired by the Genetic Algorithm (GA) is used to improve the quality of stego-image by minimizing Mean square error and morphological operation is used to extract the image component from the extracted secret image. Simulation results of the proposed steganography technique are also presented. The projected method is calculated on different parameters of image visual quality measurements

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13

Yu, J. F., C. I. Chen, C. L. Fan, and C. K. Chen. "Smoothing for the Optimal Surface of a 3D Image Model of the Human Ossicles." Journal of Mechanics 27, no. 3 (August 31, 2011): 431–36. http://dx.doi.org/10.1017/jmech.2011.45.

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ABSTRACTThis study assessed the optimal process for the surface smoothing of 3D image models of in vivo human ossicles. A 3D image model of the ossicles was reconstructed from high resolution computed tomography imaging data. Three smoothing methods including constrained smoothing, unconstrained smoothing and smoothsurface will be discussed. The volume of the 3D image model produced by unconstrained smoothing differed substantially from the original model volume prior to smoothing. Constrained smoothing had an uneven effect on the surface of the 3D image models. Using the smoothsurface module, we were able to obtain an optimal surface of the 3D image model of the human ossicles including the malleus, incus and stapes, using 20 iterations and a λ value of 0.6.

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14

Shang, Ronghua, Junkai Lin, Licheng Jiao, and Yangyang Li. "SAR Image Segmentation Using Region Smoothing and Label Correction." Remote Sensing 12, no. 5 (March 2, 2020): 803. http://dx.doi.org/10.3390/rs12050803.

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The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

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15

Altay Açar, S., and Ş. Bayır. "PRE-PROCESSES FOR URBAN AREAS DETECTION IN SAR IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 15–17. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-15-2017.

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In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually.

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16

Nazir, F., M. M. Riaz, A. Ghafoor, and F. Arif. "Brief Communication: Contrast-stretching- and histogram-smoothness-based synthetic aperture radar image enhancement for flood map generation." Natural Hazards and Earth System Sciences 15, no. 2 (February 5, 2015): 273–76. http://dx.doi.org/10.5194/nhess-15-273-2015.

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Abstract. Synthetic-aperture-radar-image-based flood map generation is usually a challenging task (due to degraded contrast). A three-step approach (based on adaptive histogram clipping, histogram remapping and smoothing) is proposed for generation of a more visualized flood map image. The pre- and post-flood images are adaptively histogram equalized. The hidden details in difference image are enhanced using contrast-based enhancement and histogram smoothing. A fast-ready flood map is then generated using equalized pre-, post- and difference images. Results (evaluated using different data sets) show significance of the proposed technique.

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17

Li, Pei, Hongjuan Wang, Mengbei Yu, and Yeli Li. "Overview of Image Smoothing Algorithms." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012024. http://dx.doi.org/10.1088/1742-6596/1883/1/012024.

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18

A.R. Hasso, Maha, and Farah Saad Al-Mukhtar. "Image Smoothing Based On FPGA." JOURNAL OF EDUCATION AND SCIENCE 26, no. 3 (August 1, 2013): 38–51. http://dx.doi.org/10.33899/edusj.2013.89910.

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19

Fan, Qingnan, Jiaolong Yang, David Wipf, Baoquan Chen, and Xin Tong. "Image smoothing via unsupervised learning." ACM Transactions on Graphics 37, no. 6 (January 10, 2019): 1–14. http://dx.doi.org/10.1145/3272127.3275081.

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20

Eun, Hyunjun, and Changick Kim. "Superpixel-Guided Adaptive Image Smoothing." IEEE Signal Processing Letters 23, no. 12 (December 2016): 1887–91. http://dx.doi.org/10.1109/lsp.2016.2630741.

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21

Li, C. H., and C. K. Lee. "Image Smoothing Using Parametric Relaxation." Graphical Models and Image Processing 57, no. 2 (March 1995): 161–74. http://dx.doi.org/10.1006/gmip.1995.1016.

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22

Khongkraphan, Kittiya, Aniruth Phonon, and Sainuddeen Nuiphom. "An Efficient Blind Image Deblurring Using a Smoothing Function." Applied Computational Intelligence and Soft Computing 2021 (April 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/6684345.

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This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods.

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23

Sharma, Raghvendra, and B. S. Daya Sagar. "MATHEMATICAL MORPHOLOGY BASED CHARACTERIZATION OF BINARY IMAGE." Image Analysis & Stereology 34, no. 2 (June 29, 2015): 111. http://dx.doi.org/10.5566/ias.1291.

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This paper reports the results of a theoretical study on morphological characterization of foreground (X) and background (Xc) of a discrete binary image. Erosion asymmetry and dilation asymmetry, defined to elaborate smoothing of an image respectively by contraction and expansion, are generalized for multiscale smoothing, and their relationships with morphological skeleton and ridge (background skeleton) transformations are discussed. Then we develop algorithms identifying image topology in terms of critical scales corresponding to close-hulls and open-skulls, along with a few other salient characteristics, as respective smoothing by expansion and contraction proceeds. For empirical demonstration of these algorithms, essentially to unravel the hidden characteristics of topological and geometrical relevance, we considered deterministic and random binary Koch quadric fractals. A shape-size based zonal quantization technique for image and its background is introduced as analytical outcome of these algorithms. The ideas presented and demonstrated on binary fractals could be easily extended to the grayscale images and fractals.

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24

Abdulkader, Zaid A. "Fingerprint Identification System Using Half Smoothing Filters." Webology 19, no. 1 (January 20, 2022): 406–18. http://dx.doi.org/10.14704/web/v19i1/web19029.

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Fingerprints have been significantly utilized for enormous security systems for their high-level accuracy. They even have been used effectively in smartphone devices for payment in addition to the security purpose. In this paper we propose a new finger print recognition method based on ridge and valley features in finger print images. These features extracted by rotating Gaussian semi _filters. The novelty of this finger print approach based on the mixing of directional filters and differences of Gaussian filter ideas. We obtain a new ridge/valley detector enabling very precise detection of ridge/valley points. This method conducted on different fingerprints database images, the proposed method achieves recognition accuracy rate of more than 99% for three fingerprint image datasets ATVS, LivDet2009, and LivDet2011.

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25

Indriyani, T., M. I. Utoyo, and R. Rulaningtyas. "Comparison of Image Smoothing Methods on Potholes Road Images." Journal of Physics: Conference Series 1477 (March 2020): 052056. http://dx.doi.org/10.1088/1742-6596/1477/5/052056.

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26

Toet, Alexander. "Alternating guided image filtering." PeerJ Computer Science 2 (June 27, 2016): e72. http://dx.doi.org/10.7717/peerj-cs.72.

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Edge preserving filters aim to simplify the representation of images (e.g., by reducing noise or eliminating irrelevant detail) while preserving their most significant edges. These filters are typically nonlinear and locally smooth the image structure while minimizing both blurring and over-sharpening of visually important edges. Here we present the Alternating Guided Filter (AGF) that achieves edge preserving smoothing by combining two recently introduced filters: the Rolling Guided Filter (RGF) and the Smooth and iteratively Restore Filter (SiR). We show that the integration of RGF and SiR in an alternating iterative framework results in a new smoothing operator that preserves significant image edges while effectively eliminating small scale details. The AGF combines the large scale edge and local intensity preserving properties of the RGF with the edge restoring properties of the SiR while eliminating the drawbacks of both previous methods (i.e., edge curvature smoothing by RGF and local intensity reduction and restoration of small scale details near large scale edges by SiR). The AGF is simple to implement and efficient, and produces high-quality results. We demonstrate the effectiveness of AGF on a variety of images, and provide a public code to facilitate future studies.

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27

Liu, Zhang, Qi Huang, Jian Li, and Qi Wang. "Single Image Super-Resolution viaL0Image Smoothing." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/974509.

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We propose a single image super-resolution method based on aL0smoothing approach. We consider a low-resolution image as two parts: one is the smooth image generated by theL0smoothing method and the other is the error image between the low-resolution image and the smoothing image. We get an intermediate high-resolution image via a classical interpolation and then generate a high-resolution smoothing image with sharp edges by theL0smoothing method. For the error image, a learning-based super-resolution approach, keeping image details well, is employed to obtain a high-resolution error image. The resulting high-resolution image is the sum of the high-resolution smoothing image and the high-resolution error image. Experimental results show the effectiveness of the proposed method.

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28

Imanuddin, Imanuddin, Raza Oktafian, and Munawir Munawir. "Image Smoothing Menggunakan Metode Mean Filtering." JOINTECS (Journal of Information Technology and Computer Science) 4, no. 2 (July 12, 2019): 57. http://dx.doi.org/10.31328/jointecs.v4i2.1007.

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Анотація:

Pelembutan Citra (Image smoothing) bertujuan untuk menekan gangguan (noise) pada citra.Gangguan tersebut biasanya muncul sebagai akibat dari hasil penerokan yang tidak bagus (sensor noise, photographic grain noise) atau akibat saluran transmisi (pada pengiriman data).Penelitian ini telah menghasilkan sebuah program aplikasi untuk image smoothing dengan beberapa metode yaitu mean filtering,grayscale dan gaussian filtering. Citra uji yang digunakan pada penelitian ini menggunakan satu sampel gambar. Citra tersebut di-load dan ditampilkan pada program. Kemudian dilakuan proses image smoothing dengan menggunakan metode grayscale,gaussian dan mean.

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29

Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (February 1, 2023): 828. http://dx.doi.org/10.3390/rs15030828.

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Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.

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30

Zhan, Yi. "The Nonlocalp-Laplacian Evolution for Image Interpolation." Mathematical Problems in Engineering 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/837426.

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Анотація:

This paper presents an image interpolation model with nonlocalp-Laplacian regularization. The nonlocalp-Laplacian regularization overcomes the drawback of the partial differential equation (PDE) proposed by Belahmidi and Guichard (2004) that image density diffuses in the directions pointed bylocalgradient. The grey values of images diffuse along image feature direction not gradient direction under the control of the proposed model, that is, minimal smoothing in the directions across the image features and maximal smoothing in the directions along the image features. The total regularizer combines the advantages of nonlocalp-Laplacian regularization and total variation (TV) regularization (preserving discontinuities and 1D image structures). The derived model efficiently reconstructs the real image, leading to a natural interpolation, with reduced blurring and staircase artifacts. We present experimental results that prove the potential and efficacy of the method.

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31

Wu, Xinming, and Zhenwei Guo. "Detecting faults and channels while enhancing seismic structural and stratigraphic features." Interpretation 7, no. 1 (February 1, 2019): T155—T166. http://dx.doi.org/10.1190/int-2017-0174.1.

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A 3D seismic image contains structural and stratigraphic features such as reflections, faults, and channels. When smoothing such an image, we want to enhance all of these features so that they are easier to interpret. Most smoothing methods aim to enhance reflections but may blur faults and channels in the image. A few methods smooth seismic reflections while preserving faults and channel boundaries. However, it has not well-discussed to smooth simultaneously along the seismic reflections and channels, which are linear features apparent within dipping reflections. In addition, to interpret faults and channels, extra steps are required to compute attributes or mappings of faults and channels from a seismic image. Such fault and channel attributes are often sensitive to noise because they are typically computed as discontinuities of seismic reflections. In this paper, we have developed methods to simultaneously enhance seismic reflections, faults, and channels while obtaining mappings of the faults and channels. In these methods, we first estimate the orientations of the reflections, faults, and channels directly in a seismic image. We then use the estimated orientations to control the smoothing directions in an efficient iterative diffusion scheme to smooth a seismic image along the reflections and channels. In this iterative scheme, we also efficiently compute mappings of faults and channels, which are used to control smoothing extents in the diffusion to stop smoothing across them. This diffusion scheme iteratively smooths a seismic image along reflections and channels while updating the mappings of faults and channels. By doing this, we will finally obtain an enhanced seismic image (with enhanced reflections and channels and sharpened faults) and cleaned mappings of faults and channels (discontinuities related to noise are cleaned up). We have examined the methods using 2D and 3D real seismic images.

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32

Sanger, Junaidy B., Immanuela P. Saputro P. Saputro, and Yunita Komalig. "Pelembutan Citra dengan Metode Filter Gaussian." JEECOM Journal of Electrical Engineering and Computer 5, no. 1 (April 14, 2023): 101–5. http://dx.doi.org/10.33650/jeecom.v5i1.5894.

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Анотація:

Image is a multimedia component rich in information and has an essential role as an information provider. However, the images encountered often experience a decrease in quality, such as defects or noise, causing the information conveyed from these images less clear. Noise causes the image to be too contrasted, blurry, or not sharp enough. One type of noise is contrast noise. Image Smoothing here is one of the operations to improve quality which aims to smooth out images with unbalanced contrast noise. Disturbances in the image are generally in the form of variations in the intensity of a pixel that is not correlated with neighboring pixels. Contrasting images are caused by uneven lighting, which can cause the information in the image to be reduced and difficult to interpret. For this reason, quality improvement must be made to get a better image. In this study, the softening of contrasting images uses the Gaussian Filter method. This filter has the effect of equalizing the gray distance to make the image obtained smoother. Based on the results of the tests, it got an accuracy of 83.3%, meaning that the application's performance is suitable for image smoothing.

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33

Bai, Chun Lan. "Image Enhancement Based on Space Domain with MATLAB." Advanced Materials Research 403-408 (November 2011): 3063–66. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3063.

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Анотація:

Based on image enhancement-theory of space domain method, gray-scale transformation, histogram adjustment, smoothing, sharpening filters and so on, which are analyzed. On this basis, image enhancement is carried on simulation and programming to use MATLAB. Results are analyzed and compared with gray-scale transformation, histogram adjustment, smoothing, sharpening filters of space domain method, indicating better to improve the image to adopt smoothing filtering.

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34

Petrushynskyi, O., Y. Kynash, O. Riznyk, N. Kustra, and V. Myshchyshyn. "Smoothing the image using linear filtration." Computer Technologies of Printing 1, no. 45 (2021): 100–109. http://dx.doi.org/10.32403/2411-9210-2021-1-45-100-109.

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35

Li, Fang, and Yuanming Zhu. "Smoothing and Clustering Guided Image Decolorization." Image Analysis & Stereology 40, no. 1 (April 9, 2021): 17–27. http://dx.doi.org/10.5566/ias.2348.

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Анотація:

In this paper, we propose a new image decolorization method based on image clustering and weight optimization. First, we smooth the color image and cluster it into several classes and get the class centers. Each center can represent a distinctive color in the image. Then the class centers are sorted according to their brightness measured by Euclidean norm. By assuming that the decolorized grayscale image is a linear combination of the three channels of the color image, we propose an optimization problem by forcing the sorted class centers to correspond to specified grayscale values satisfying uniform distribution. Numerically, the problem is solved by quadratic programming. Experiments on two popular data sets demonstrate that the proposed method is competitive with the state-of-the-art decolorization method.

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36

Fang, Shuai, Zhenji Yao, and Jing Zhang. "Scale and Gradient Aware Image Smoothing." IEEE Access 7 (2019): 166268–81. http://dx.doi.org/10.1109/access.2019.2953550.

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37

Dou, Zeyang, Mengnan Song, Kun Gao, and Zeqiang Jiang. "Image Smoothing via Truncated Total Variation." IEEE Access 5 (2017): 27337–44. http://dx.doi.org/10.1109/access.2017.2773503.

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38

Ramponi, G. "The rational filter for image smoothing." IEEE Signal Processing Letters 3, no. 3 (March 1996): 63–65. http://dx.doi.org/10.1109/97.481156.

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39

Ding, Feng, Yuxi Shi, Guopu Zhu, and Yun-Qing Shi. "Smoothing identification for digital image forensics." Multimedia Tools and Applications 78, no. 7 (November 6, 2018): 8225–45. http://dx.doi.org/10.1007/s11042-018-6807-6.

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40

Wang, Jie, Yongzhen Wang, Yidan Feng, Lina Gong, Xuefeng Yan, Haoran Xie, Fu Lee Wang, and Mingqiang Wei. "Contrastive Semantic‐Guided Image Smoothing Network." Computer Graphics Forum 41, no. 7 (October 2022): 335–46. http://dx.doi.org/10.1111/cgf.14681.

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41

Oishi, Shuji, Ryo Kurazume, Yumi Iwashita, and Tsutomu Hasegawa. "Smoothing Range Image using Trilateral Filter and Reflectance Image." IEEJ Transactions on Electronics, Information and Systems 132, no. 2 (2012): 291–98. http://dx.doi.org/10.1541/ieejeiss.132.291.

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42

Chochia, P. A. "Two–Scale Image Analysis in the Image Smoothing Problem." Procedia Engineering 201 (2017): 223–30. http://dx.doi.org/10.1016/j.proeng.2017.09.619.

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43

Livingston, Charles. "Chiral smoothings of knots." Proceedings of the Edinburgh Mathematical Society 63, no. 4 (November 2020): 1048–61. http://dx.doi.org/10.1017/s0013091520000322.

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Анотація:

AbstractCan smoothing a single crossing in a diagram for a knot convert it into a diagram of the knot's mirror image? Zeković found such a smoothing for the torus knot T(2, 5), and Moore–Vazquez proved that such smoothings do not exist for other torus knots T(2, m) with m odd and square free. The existence of such a smoothing implies that K # K bounds a Mobius band in B4. We use Casson–Gordon theory to provide new obstructions to the existence of such chiral smoothings. In particular, we remove the constraint that m be square free in the Moore–Vazquez theorem, with the exception of m = 9, which remains an open case. Heegaard Floer theory provides further obstructions; these do not give new information in the case of torus knots of the form T(2, m), but they do provide strong constraints for other families of torus knots. A more general question asks, for each pair of knots K and J, what is the minimum number of smoothings that are required to convert a diagram of K into one for J. The methods presented here can be applied to provide lower bounds on this number.

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44

Yang, Gang. "Image Mosaic Algorithm Study." Advanced Materials Research 446-449 (January 2012): 3857–60. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.3857.

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In order to improve the veracity and speed of image mosaic, Gave a template matching algorithm based on area of picture. The algorithm inherited many merits of the image matching algorithm based on area and improved speed of matching. It firstly defined mode in an image, then searched best similarity match in another image, Got the translation parameters between the images, ranged and matched images based parameters,and dealt with the overlapping of smoothing, so it improved the speed and image mosaics effect.Fially the validity of algorithm was checked in visual C++.

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45

Bai, Xiaoming, Xue Mi, Hai Xie, Kaikai Shi, Furui Xiong, Yixiong Zhang, and Licheng Guo. "An Image-Based Double-Smoothing Cohesive Finite Element Framework for Particle-Reinforced Materials." Mathematics 8, no. 4 (April 7, 2020): 543. http://dx.doi.org/10.3390/math8040543.

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In order to simulate the fracture process of particle-reinforced materials on the micro-scale, an image-based double-smoothing cohesive finite element framework is proposed in the present paper. Two separate smoothing processes are performed to reduce the noise in the digital image and eliminate the jagged elements in the finite element mesh. The main contribution of the present study is the proposed novel image-based cohesive finite element framework, and this method improved the quality of the meshes effectively. Meanwhile, the artificial resistance due to the jagged element is reduced with the double-smoothing cohesive finite element framework during the crack propagation. Therefore, the image-based double-smoothing cohesive finite element framework is significant for the simulation of fracture mechanics.

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46

Liu, Wei, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, and Ian Reid. "A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11620–28. http://dx.doi.org/10.1609/aaai.v34i07.6830.

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Анотація:

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications.

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47

Zhang, Ming Jun, and Xing Qi Yuan. "Study on Filtering Algorithm of Image Using Matlab." Applied Mechanics and Materials 239-240 (December 2012): 1173–78. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1173.

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Анотація:

To increase signal to noise ratio (SNR) and to stress on expectation characters, an improved adaptive minutia preserving smoothing algorithm is proposed using Matlab based on multi-scale and multidirectional masks. This algorithm keeps the mask’s good performance in preserving details. It divides image into sub-images according to the statistics from image gradation-gradient histogram, and the adaptive threshold value generate according to the gradient information of the whole and the local image. This method deals with the difficulty of choosing threshold and improves the automation of image smoothing. The results of experiments show that the improved algorithm has more better performance than that classical algorithm either in reducing noise efficiently in intra-region and at edges or in keeping line-like structures such as edges and textures.

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48

Abid Hasan, Syed Mohammad, and Kwanghee Ko. "Depth edge detection by image-based smoothing and morphological operations." Journal of Computational Design and Engineering 3, no. 3 (February 17, 2016): 191–97. http://dx.doi.org/10.1016/j.jcde.2016.02.002.

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Abstract Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc. Highlights A method is proposed that can detect edges from depth images more profoundly. We modified the Canny edge detection method using morphological operations. The proposed method works in multi-frames.

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49

Vizilter, Y. V., A. Y. Rubis, and S. Y. Zheltov. "CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 403–10. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-403-2017.

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Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

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50

Demir, Y., and N. H. Kaplan. "Low-light image enhancement based on sharpening-smoothing image filter." Digital Signal Processing 138 (June 2023): 104054. http://dx.doi.org/10.1016/j.dsp.2023.104054.

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