scholarly journals Image Denoising Using Discrete Wavelet Transform : A Theoretical Framework

2018 ◽  
Vol 7 (2.16) ◽  
pp. 120
Author(s):  
Praveen Bhargava ◽  
Shruti Choubey ◽  
Rakesh Kumar Bhujade ◽  
Nilesh Jain
Keyword(s):  
Image Denoising ◽  
Ultra Sound ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Hybrid Filter ◽  
Edge Sharpness ◽  
In Series ◽  
Salt And Pepper ◽  
Image Type

Noise is a random variation in brightness and color in image or simply we can say that unwanted signals are called noise. The noise is mixed with original signal and cause may troubles. Due to the presence of noise, quality of image is reduced and other features like edge sharpness and pattern recognition are badly affected. In image denoising methods to improve the results a hybrid filter is used for better visualization. The hybrid filter is composed with the combination of three filters connected in series. The hybridization has performed much better in case of salt and pepper type of noise and for most of the medical image type, either MRI, CT, SPECT, Ultra Sound. PSNR values show major improvement in comparison of other existing methods. Future, the results obtained from the presented denoising experiments would be tried to be improved further by using this method with other transform domain methods. Finally, the results are concluded that the proposed approach in terms of PSNR, MSE improvement is outperformed. 

scholarly journals Joint method using Akamatsu and discrete wavelet transform for image restoration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jihad Maulana Akbar ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Image Restoration ◽  
Wavelet Transformation ◽  
Digital Content ◽  
Discrete Wavelet ◽  
Additional Noise ◽  
Restoration Method ◽  
Blurred Images ◽  
Salt And Pepper ◽  
Excessive Noise

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

scholarly journals An Optimized Hybrid Filter designing for Speckle and Gaussian Noise Reduction in Ultra-Sound Images

2019 ◽  
Vol 8 (9S) ◽  
pp. 836-842
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Image Noise ◽  
Ultra Sound ◽  
Ultrasound Images ◽  
Noisy Image ◽  
Performance Parameters ◽  
Hybrid Filter ◽  
Image Region ◽  
Information Image

Image which is a visual perception of a scene or something is a set of pixels that have certain values which appear in the form of colors to view that particular set of pixel as image. Image contains information about whatever is being depicted in the picture and hence it can be said as a useful source for storing or conveying information. Image noise is apparent in image region with low signal level, such as shadow region or under exposed images. The work presents the concept of noising and denoising in a digital image. Noise is a kind of disturbance that occurs in the channel at the time of transmission. Image denoising is the procedure of improving true images from the noisy images. For ages researchers have been proposing several techniques that were used to remove the noise from the image. Image denoising is the procedure of improving true image from the noisy image. At the time of such process it is difficult to reduce noise. Owing to this difficulty, numerous denoising models have been proposed [4]. The paper presents the reduction of speckle and Gaussian noise in the biomedical ultrasound images. In the proposed work, the Butterworth filter is applied for filtering the noisy image and then the coefficients are optimized by using the firefly algorithm mechanism to remove noise that occurs at the time of transmission and then it is hybrid with the Weiner filter. Experiments have been performed to check the performance of the proposed technique. The results are analyzed quantatively using PSNR and SSIM. The results are also evaluated on other performance parameters such as BER, MSE and fitness on speckle as well as on Gaussian noise.

Status of nondestructive control of transport function metal products at JSC EVRAZ NTMK

2020 ◽  
Vol 76 (6) ◽  
pp. 586-590
Author(s):  
S. P. Bersenev ◽  
E. M. Slobtsova
Keyword(s):  
Crack Detection ◽  
Middle Part ◽  
Control Line ◽  
Magnetic Powder ◽  
Nondestructive Control ◽  
Automated Control ◽  
Ultrasonic Control ◽  
Acoustic Contact ◽  
In Series

Achievements in the area of automated ultrasonic control of quality of rails, solid-rolled wheels and tyres, wheels magnetic powder crack detection, carried out at JSC EVRAZ NTMK. The 100% nondestructive control is accomplished by automated control in series at two ultrasonic facilities RWI-01 and four facilities УМКК-1 of magnetic powder control, installed into the exit control line in the wheel-tyre shop. Diagram of location, converters displacement and control operations in the process of control at the facility RWI-01 presented, as well as the structural diagram of the facility УМКК-1. The automated ultrasonic control of rough tyres is made in the tyres control line of the wheel-tyre shop at the facility УКБ-1Д. The facility enables to control internal defects of tyres in radial, axis and circular directions of radiation. Possibilities of the facility УКБ-1Д software were shown. Nondestructive control of railway rails is made at two facilities, comprising the automated control line of the rail and structural shop. The УКР-64Э facility of automated ultrasonic rails control is intended to reveal defects in the area of head, web and middle part of rail foot by pulse echo-method with a immersion acoustic contact. The diagram of rail P65 at the facility УКР-64Э control presented. To reveal defects of the macrostructure in the area of rail head and web by mirror-shadow method, an ultrasonic noncontact electromagnetic-acoustic facility is used. It was noted, that implementation of the 100% nondestructive control into the technology of rolled stuff production enabled to increase the quality of products supplied to customers and to increase their competiveness.

Image Denoising Using Hybrid Filter

2012 ◽  
Author(s):  
Vinod Kumar ◽  
Anil Kumar ◽  
Pushparaj Pal
Keyword(s):  
Image Denoising ◽  
Hybrid Filter

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

FUSION OF SURVEILLANCE IMAGES IN INFRARED AND VISIBLE BAND USING CURVELET, WAVELET AND WAVELET PACKET TRANSFORM

2010 ◽  
Vol 08 (02) ◽  
pp. 271-292 ◽  
Author(s):  
PARUL SHAH ◽  
S. N. MERCHANT ◽  
U. B. DESAI
Keyword(s):  
Wavelet Packet ◽  
Curvelet Transform ◽  
Visual Quality ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images

This paper presents two methods for fusion of infrared (IR) and visible surveillance images. The first method combines Curvelet Transform (CT) with Discrete Wavelet Transform (DWT). As wavelets do not represent long edges well while curvelets are challenged with small features, our objective is to combine both to achieve better performance. The second approach uses Discrete Wavelet Packet Transform (DWPT), which provides multiresolution in high frequency band as well and hence helps in handling edges better. The performance of the proposed methods have been extensively tested for a number of multimodal surveillance images and compared with various existing transform domain fusion methods. Experimental results show that evaluation based on entropy, gradient, contrast etc., the criteria normally used, are not enough, as in some cases, these criteria are not consistent with the visual quality. It also demonstrates that the Petrovic and Xydeas image fusion metric is a more appropriate criterion for fusion of IR and visible images, as in all the tested fused images, visual quality agrees with the Petrovic and Xydeas metric evaluation. The analysis shows that there is significant increase in the quality of fused image, both visually and quantitatively. The major achievement of the proposed fusion methods is its reduced artifacts, one of the most desired feature for fusion used in surveillance applications.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

2007 ◽  
Vol 16 (8) ◽  
pp. 2080-2095 ◽  
Author(s):  
Kostadin Dabov ◽  
Alessandro Foi ◽  
Vladimir Katkovnik ◽  
Karen Egiazarian
Keyword(s):  
Collaborative Filtering ◽  
Image Denoising ◽  
Transform Domain
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