Distinction of Glossy Colored Objects Using Gray Level

1992 ◽  
Vol 4 (6) ◽  
pp. 511-519
Author(s):  
Kazuo Yamaba ◽  
◽  
Yoichi Miyake ◽  
Keyword(s):  
Color Image ◽  
Wiener Filter ◽  
Psychophysical Method ◽  
Filter Method ◽  
Zoom Lens ◽  
Measuring Apparatus ◽  
Blurred Image ◽  
Mirror Box ◽  
Tristimulus Values ◽  
Lamp Illumination

A new measuring apparatus for color images has been developed in order to distinguish glossy colored objects under fluorescent lamp illumination. The color image apparatus is mainly composed of a zoom lens, a mirror box, MOS cameras, a microcomputer and a color image processor. The zoom lens can automatically blur an image by the microcomputer. It is a very effective method for obtaining a blurred image in detecting glossy colored objects. Gloss can be omitted by blurring an original image of objects. In the blurred image, it is known that tristimulus values are not affected by image restoration if the modified Wiener filter method is employed. The blurred image is restored by the filter and is processed by a new analogical method utilizing a fuzzy set theory based on a visual psychophysical method. As a result of this experiment, it can be concluded that the system demonstrates the possibility of highly accurate distinction of glossy colored objects.

Restoration of Motion Blurred Image Based on PSO Combine Wiener Filter in Ship Imaging System

2014 ◽  
Vol 1006-1007 ◽  
pp. 739-742
Author(s):  
Hui Xuan Fu ◽  
Yu Chao Wang ◽  
Xun Su
Keyword(s):  
Imaging System ◽  
Search Algorithm ◽  
Wiener Filter ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Improve Image Quality ◽  
Blurred Image ◽  
Blurred Images ◽  
Ringing Effect

Ship internal equipment vibration will cause the imaging system platform vibration, resulting in blurred images. Wiener Filter is often used to restore the motion blurred image. The principle of the method expects to minimize the mean square error between the restore image and original image. However, this method has some constrains, if parameter selection improper, it generates ringing effect easily. Usually, most users select parameter by rule of thumb, so they frequently fail to generate the optimal solution. In order to get high quality restore image, eliminate the ringing effect, a new approach based on particle swarm optimization (PSO) Wiener Filter was proposed, which automatically adjusts the parameter for Wiener Filter, this method seek the optimal solution by transferring information between individuals and information sharing, which is a highly efficient parallel search algorithm, insuring the accuracy of parameter selection, effectively reducing the ringing effect after image restoration, improve image quality of restoration.

scholarly journals Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

2011 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
Ika Purwanti Ningrum ◽  
Agfianto Eko Putra ◽  
Dian Nursantika
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Color Image ◽  
Median Filter ◽  
Bilateral Filter ◽  
Filter Method ◽  
Noise Filtering ◽  
Original Image ◽  
Average Filter

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper

Simulating Chalk Art Style Painting

2017 ◽  
Vol 31 (12) ◽  
pp. 1759026
Author(s):  
Wenhua Qian ◽  
Dan Xu ◽  
Zheng Guan ◽  
Kun Yue ◽  
Yuanyuan Pu
Keyword(s):  
Color Image ◽  
Filter Method ◽  
Source Image ◽  
Nonphotorealistic Rendering ◽  
Edge Image ◽  
The Real ◽  
Integral Convolution ◽  
Line Integral Convolution ◽  
Application Fields ◽  
Art Style

Different kinds of illustrations and artistic imagery can be generated or simulated through the nonphotorealistic rendering (NPR) technique. However, designing and simulating new NPR artistic styles remains extremely challenging. Chalk art style is a very famous artistic work all over the world, and few algorithms have been put forward to illustrate this style. This paper presents a novel NPR technique which generates a chalk art drawing from a 2D photograph automatically. We aim at obtaining a set of lines surface with coarse appearance and generating stroke textures of the real chalk painting. Firstly, the edge of the source image is extracted by difference-of-Gaussian filter method. To simulate chalk painting’s lines, image diffusion and enhancement techniques are proposed to produce coarse and rough lines. Secondly, we developed an improved line integral convolution and dilation operation methods to produce the chalk stroke texture. Finally, the edge image, stroke texture image and color image will be mapped to another background image to generate the chalk art drawing. Experimental results are presented to show the effectiveness of our method in producing the color chalk stylistic illustrations, and the methods can simulate the characters of the real chalk art painting. The proposed method of this paper will enlarge the research and application fields of NPR. Meanwhile, it provides a tool for the user to create chalk art paintings via computers even without painting skill.

The Restoration of Motion Blurred Images Based on the Background Modeling

2014 ◽  
Vol 687-691 ◽  
pp. 3591-3595
Author(s):  
Jiang Yang Chen ◽  
Xi Ling Luo
Keyword(s):  
Wiener Filter ◽  
Background Modeling ◽  
Motion Blur ◽  
Edge Density ◽  
Image Generation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Image Area ◽  
Blurred Images

For the mutual effects of camera shake and subject movement, the image generation space varying motion blur. In order to achieve image restoration, firstly dividing the image area using the Gaussian background modeling, and updated model adaptive to improve the speed and convergence accuracy. Then use the total variation (TV) of the L1 model to estimate the regional point spread function (PSF), and adopted the edge density weight to reduce small edge’s interference for the PSF estimates. Eventually to restored image by Wiener filter. Through experimental analysis, compared with other algorithms, our algorithms get better results in the space varies motion-blurred image.

scholarly journals Multichannel speaker interference reduction using frequency domain adaptive filtering

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Patrick Meyer ◽  
Samy Elshamy ◽  
Tim Fingscheidt
Keyword(s):  
Wiener Filter ◽  
Echo Cancellation ◽  
Filter Method ◽  
Noise Component ◽  
Interference Reduction ◽  
Live Music ◽  
Audio Recordings ◽  
Automatic Transcription ◽  
Component Estimation ◽  
Filter Approach

Abstract Microphone leakage or crosstalk is a common problem in multichannel close-talk audio recordings (e.g., meetings or live music performances), which occurs when a target signal does not only couple into its dedicated microphone, but also in all other microphone channels. For further signal processing such as automatic transcription of a meeting, a multichannel speaker interference reduction is required in order to eliminate the interfering speech signals in the microphone channels. The contribution of this paper is twofold: First, we consider multichannel close-talk recordings of a three-person meeting scenario with various different crosstalk levels. In order to eliminate the crosstalk in the target microphone channel, we extend a multichannel Wiener filter approach, which considers all individual microphone channels. Therefore, we integrate an adaptive filter method, which was originally proposed for acoustic echo cancellation (AEC), in order to obtain a well-performing interferer (noise) component estimation. This results in an improved speech-to-interferer ratio by up to 2.7 dB at constant or even better speech component quality. Second, since an AEC method requires typically clean reference channels, we investigate and report findings why the AEC algorithm is able to successfully estimate the interfering signals and the room impulse responses between the microphones of the interferer and the target speakers even though the reference signals are themselves disturbed by crosstalk in the considered meeting scenario.

scholarly journals Image Deblurring using Wiener Filtering and Siamese Neural Network

2021 ◽  
Vol 9 (3) ◽  
pp. 96
Author(s):  
Vaibhav Setia ◽  
Shreya Kumar
Keyword(s):  
Neural Network ◽  
Image Matching ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Function Estimation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Local Image

Blurred images are difficult to avoid in situations when minor Atmospheric turbulence or camera movement results in low-quality images. We propose a system that takes a blurred image as input and produces a deblurred image by utilizing various filtering techniques. Additionally, we utilize the Siamese Network to match local image segments. A Siamese Neural Network model is used that is trained to account for image matching in the spatial domain. The best-matched image returned by the model is then further used for Signal-to-Noise ratio and Point Spread Function estimation. The Wiener filter is then used to deblur the image. Finally, the results of the deblurring techniques with existing algorithms are compared and it is shown that the error in deblurring an image using the techniques presented in this paper is considerably lesser than other techniques.

scholarly journals Application of powerline noise cancellation method in correlation identification of controlled source electromagnetic method

2021 ◽  
Vol 18 (3) ◽  
pp. 339-354
Author(s):  
Zhi Yang ◽  
Jingtian Tang ◽  
Xiao Xiao ◽  
Qiyun Jiang ◽  
Xiangyu Huang ◽  
...  
Keyword(s):  
System Identification ◽  
Estimation Method ◽  
Wiener Filter ◽  
Noise Estimation ◽  
Identification Accuracy ◽  
Electromagnetic Method ◽  
Filter Method ◽  
Noise Elimination ◽  
Controlled Source ◽  
Transmitted Signal

Abstract Powerline interference in the controlled source electromagnetic method has traditionally been one of the biggest conundrums plaguing geophysicists, and its conventional denoising methods primarily include filtering and noise estimation. The filter method leaches noise at specific frequency points, which might also filter useful signals; the noise estimation method significantly eliminates interference, whereas the premise is that the noise is stable after a short time and a recorder is necessary in the field. In the present study, using the periodicity and symmetry of powerline noise, we propose a subtraction and an addition method for cancellation of the powerline noise. First, the transmitted signal is optimized so that the equivalent transmitted signal is an m sequence; then the response signal is processed by using the cancellation method; subsequently, the correlation identification is applied and finally, we solve the earth impulse response by means of the Wiener filter deconvolution method. Simulation experiments and field data tests demonstrate that the powerline noise can be well suppressed by the cancellation method proposed in the present study, so that the system identification accuracy is greatly improved. The method is simple in principle and effective in removing powerline noise, which presents a novel perspective on noise elimination for system identification.

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