Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

Sun, Xin; He, Ning; Zhang, Yuqing; Zhen, Xueyan; Lü, Ke; Zhou, Xin

doi:10.1155/2017/5835020

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…In this study color image was filtered without changing into gray scale image, because color image contains better visual effects than gray image in terms of visual perception, the edge information of color image is more abundant than gray images [62]. Color and texture features are fundamental features to identify different precancerous regions in the image of the cervix [63].…”

Section: Bilateral Filtermentioning

confidence: 99%

Cervical precancerous lesion and cancer detection on Pap smear image using machine learning algorithm

Getenet,

Kassaw

2023

Preprint

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Cervical cancer is the second most common cancer in women globally, it is the leading cause of female death, next to breast cancer. Sexually transmitted virus, known as Human papillomavirus, causes this cancer. This preventable diseases cause female death because of lack of cervical screening in health institutes. Cervical screening used to detect the precancerous lesion before developing cancer cells. Pap smear is one of cervical cancer screening techniques that uses microscope to visualize the cervix lesion or cervix cancer. However, visual inspection suffers from false positive or false negative results due to human errors. This research aims to change the visual inspection to Computer-Assisted Screening using machine-learning algorithm. Machine learning algorithm currently used for the detection and diagnosis of cervical cancer. The ultimate objective of this research is to detect precancerous lesion before developing cancer cells using multi-class classification, on local Pap smear image data. In this research, 1224 Pap smear image collected from local health institute and annotated by pathologist. Preprocessing mainly focused on image denoising using bilateral filter to remove Poisson noise. Convolutional Neural Network (CNN) and Pre-trained VGG19 algorithms were developed using train, validation and test data split. From Classical machine learning, Support Vector Machine (SVM) and Random Forest (RF) algorithms were developed. The accuracy of CNN, VGG-19, SVM and RF are 99%, 100%, 96% and 100% registered, respectively. Pre-trained VGG19 and Random Forest models were outperformed. Hence, these developed algorithms can improve the diagnosis and detection of cervical lesion and cancer cells from Pap smear images, which are safe, simple, available and routine screening methods in cervical diagnosis to improve the quality of life of patients.

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Section: Bilateral Filtermentioning

confidence: 99%

Cervical precancerous lesion and cancer detection on Pap smear image using machine learning algorithm

Getenet,

Kassaw

2023

Preprint

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“…The average filter removes this type of blur. It is useful when noise is covering the whole image [11,25].…”

Section: Average Blurmentioning

confidence: 99%

“…Reconstruction of noisy blurred images using blur kernel by Ellappan and Chopra [10] are used Lucy Richardson algorithm which is also named a non-blind algorithm for making the image less noisy. Sun, et al, [11] presented color image denoising based on the guided filter and wavelet threshold. They used image denoising based on the adaptive wavelet threshold and guided filtering.…”

Section: Introductionmentioning

confidence: 99%

Restoration for blurred noisy images based on guided filtering and inverse filter

Al_taie

2021

IJECE

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The development of complex life leads into a need using images in several fields, because these images degraded during capturing the image from mobiles, cameras and persons who do not have sufficient experience in capturing images. It was important using techniques differently to improve images and human perception as image enhancement and image restoration etc. In this paper, restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed. In the color images denoising process, it was very significant for improving the edge and texture information. Eliminating noise can be enhanced by the image quality. In this article, at first, The color images were taken. Then, random noise and blur were added to the images. Then, the noisy blurred image passed to the guided filtering to get on denoised image. Finally, an inverse filter applied to the blurred image by convolution an image with a mask and getting on the enhanced image. The results of this research illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure. Also, it enhanced the image and retained the edge details in the denoising process. PSNR and SSIM measures were more sensitive to Gaussian noise than blur.

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“…Xin Zhang proposed a depth image denoising and enhancement framework utilizing a light convolution network [39]. Xin Sun has presented Colour Image Denoising Predicated on Guided Filter and Adaptive Wavelet Threshold [40].…”

Section: Related Literature Surveymentioning

confidence: 99%

A Novel Algorithm based on UBTMF for Colour Pictures

Rehani¹,

Saini²

2018

IJARCSSE

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The issue of picture denoising is one of the most established in the field, is as yet getting extensive focus from the exploration zone due to consistently expanding interest for sensibly valued great media and in additament its part as a pre-preparing venture for picture division, pressure, and so on, because of high spatial being without a vocation of mundane pictures, nearby averaging of the pixels impressively abate the commotion while bulwark the first structure of the picture. To enhance the execution of the essential channels, more compelling sifting calculations including the exchanging vector channels and the amalgamation vector. In spite of the fact that there are different sifting calculations to cull, the more preponderant part of them is not outfit predicated. Multifarious Median Filter (AMF) performs well at low commotion densities. Be that as it may, at high filter densities the window measure must be expanded which may prompt obscuring the picture. In exchanging middle channel the cull depends on Re-characterized limit esteem. The paramount downside of this technique is that characterizing a vigorous cull is onerous. Supplementally these channels won't consider the nearby highlights because of which points of interest and edges may not be recouped severely, concretely when the filter level is high. To vanquish the above downside, Decision Predicated Algorithm (DBA) is proposed. In this, the picture is denoised by utilizing a 3x3 window. On the off chance that the preparing pixel esteem is 0 or 255 it is handled or else it is left unaltered. At high commotion thickness the middle esteem will be 0 or 255 which is boisterous. The goal of disuniting is to expel the driving forces so the commotion free picture is planarity recouped with least flag bending. Filter (Clamor) expulsion can be accomplished by utilizing sundry subsisting direct dissevering procedures which are main stream as a result of their numerical straightforwardness and the presence of the assembling direct framework hypothesis. In spite of the fact that middle channels expel motivation clamor without harming the edges, the prodigious majority of them work consistently over the picture and in this way have a propensity to alter both commotion and clamor free pixels. Preferably, the disuniting ought to be connected just to debased pixels while leaving uncorrupted pixels in place. We propose a novel calculation for clamor diminishment in light of UBTMF for Colour pictures.

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Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

Cited by 8 publications

References 20 publications

Cervical precancerous lesion and cancer detection on Pap smear image using machine learning algorithm

Cervical precancerous lesion and cancer detection on Pap smear image using machine learning algorithm

Restoration for blurred noisy images based on guided filtering and inverse filter

A Novel Algorithm based on UBTMF for Colour Pictures

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