scholarly journals Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage

1998 ◽  
Vol 7 (3) ◽  
pp. 319-335 ◽  
Author(s):  
A. Chambolle ◽  
R.A. De Vore ◽  
Nam-Yong Lee ◽  
B.J. Lucier
Keyword(s):  
Image Processing ◽  
Variational Problems ◽  
Noise Removal ◽  
Wavelet Shrinkage ◽  
Wavelet Image

Super-Resolution Analysis on Molten Pool Image of Metal Active-Gas Welding Based on Wavelet with Fractal

2011 ◽  
Vol 103 ◽  
pp. 152-157
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Chao Yi Dong
Keyword(s):  
Image Processing ◽  
Super Resolution ◽  
Image Interpolation ◽  
Image Resolution ◽  
Mag Welding ◽  
Resolution Image ◽  
Fractal Image ◽  
Welding Image ◽  
Resolution Analysis ◽  
Wavelet Image

According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively raises MAG welding image resolution, but also PSNR of reconstruction images are enhanced 21.1049 dB.

Study of Noise Removal Techniques for Digital Images

Biometrics ◽  
2017 ◽  
pp. 1105-1144
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Study of Noise Removal Techniques for Digital Images

2016 ◽  
pp. 48-87
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

scholarly journals Comparison of preprocessing techniques for coin recognition using image processing methods

2018 ◽  
Vol 7 (2.21) ◽  
pp. 351
Author(s):  
T Hemapriya ◽  
K S. Archana ◽  
T Anupriya
Keyword(s):  
Image Processing ◽  
Texture Feature ◽  
Noise Removal ◽  
Daily Routine ◽  
Image Size ◽  
Processing Methods ◽  
Filter Noise ◽  
Clear Information ◽  
Filtering Techniques

Coin is very important role in human’s day life. For daily routine like shop, super market, banks etc the coins to be used. The coin is important part of economies and currency and it is used to pay for goods and also for our needs. Here the Indian coin has many number of count five rupee, ten rupee, two rupee, from this any one of the coin we are going to extract the texture feature for our Indian coin, first step is to preprocess the image is that method to enhance the image and remove the noise from enhanced image. For extracting clear information the image has to be preprocessed through some of the filtering techniques such as image size has to be resized, changing the contrast of the image, changing RGB to grayscale conversion for further operation such as segmentation and classification. At last the values to be compared by using PSNR, SNR, MSE of Filter noise removal with respective coin images.  

2D DWT VLSI architecture for wavelet image processing

1998 ◽  
Vol 34 (6) ◽  
pp. 537 ◽  
Author(s):  
Seung-Kwon Paek ◽  
Lee-Sup Kim
Keyword(s):  
Image Processing ◽  
Vlsi Architecture ◽  
Wavelet Image

scholarly journals An Enhanced Approach for Automation the Diagnosis of Iron Deficiency Anemia Based on Quantitative Analysis of Red Blood Cells in Intestine Villi Tissue

2018 ◽  
Vol 12 (12) ◽  
pp. 65
Author(s):  
Manar Rizik Al-Sayyed ◽  
Faten Hamad ◽  
Rizik Al-Sayyed ◽  
Hussam N. Fakhouri
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Region Of Interest ◽  
Noise Removal ◽  
Successful Outcome ◽  
Deficiency Anemia ◽  
Microscopic Images ◽  
The Status

Recent years have witnessed a huge revolution in developing automated diagnosis for different diseases such as cancer using medical image processing. Many researchers have been conducted in this field. Analyzing medical microscopic images provide pathology medical track with large information about the status of the patients and the progress of the diseases and help in detecting any pathological changes in tissues. Automation of the diagnosis of these images will lead to a better, faster and enhanced diagnosis for different hematological and histological images. This paper proposes an automated approach for analyzing blood smear microscopic images to help in diagnosing anemia using quantitative analysis of red blood cells in intestine villi tissue. The diagnoses depends on counting the number of blue and red stained blood cells that contain iron in each villi separately, then, it calculates the percentage of blue cells and red cells in the experimented image. The experimental results have shown that using digital image processing techniques through processing the image into different stages as including noise removal, image sharpening, enhancing contrast, find region of interest, isolating color, removing edges, and counting cells leads to a successful outcome and the diagnose of anemia.

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