An edge-preserving adaptive image denoising

Wang, Xiangyang; Liu, Yang-Cheng; Zhang, Na; Chang-jian, WU; Yang, Heng

doi:10.1007/s11042-014-2258-x

Cited by 11 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [5] proposed a novel denoising technique based on Non-Subsampled Shearlet Transform (NSST) and Fuzzy Support Vector Machines (FSVMs) that suppresses the noise from an image when preserving its features. Initially, the noisy image was decomposed into many subbands of frequency and orientation responses using NSST.…”

Section: Literature Surveymentioning

confidence: 99%

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Indupriya*,

Vijendran

2019

IJITEE

View full text Add to dashboard Cite

The most essential process in image processing applications is eliminating the noisy pixels from the images and acquiring the noise-free images with better reconstructed quality. This process has high complexity due to the segmentation of image patches from the noisy images. To simplify this process, Neuro-Fuzzy filtering with Optimum Adaptive parameterized Mask Non-Harmonic Analysis in Curvelet Transform domain (NF-OAMNHA-CT) has been proposed for image denoising. It uses NF edge detector instead of canny edge detector for segmenting and preserving the edge regions including homogeneous texture boundaries with minimized edge distortion. However, it requires gradient information about the error function with respect to the parameter measures during training of NF for optimizing their parameters. Also, it often affects with the fixed local minima. Therefore in this article, Recurrent Polak- Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy (RPCGNF-OAMNHA-CT) technique is proposed to enhance the edge-preserving segmentation in image denoising. At first, the noisy images are defined in the CT domain and fed to the RPCGNF edge detector as edge-preserving segmentation that segments and extracts the edge regions as well as homogeneous texture regions. In this RPCGNF technique, recurrent mechanism is applied to construct PCGNF as an updated edgepreserving segmentation technique that learns their parameters with high efficiency and speeding up the convergence. As a result, the edge regions and homogeneous textures are synchronously segmented as noisy pixels until either the PCGNF converges to the desired accuracy of segmentation or a termination criterion is reached. Further, OAMNHA is applied on each segment to remove the noisy pixels from the images and obtain the noise-free images accurately. Finally, the experimental results illustrate the proposed RPCGNFOAMNHA-CT technique achieves higher efficiency than the NFOAMNHA-CT technique in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE) and Structural Similarity (SSIM).

show abstract

Section: Literature Surveymentioning

confidence: 99%

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Indupriya*,

Vijendran

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…The squared value of SD is variance x . 3 In case of digital pulse transmission, the images are affected by impulse noise or salt-and-pepper noise. The probability density function can be written as …”

Section: Introductionmentioning

confidence: 99%

Wavelet transform approach for image processing – A research motivation for engineering graduates

Vimala

Priya

Subramani

2019

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

The study of wavelet transform and its application to image processing are being incorporated in undergraduate and postgraduate syllabuses. This article discuss the basic concepts of image processing and its importance in real time application. The content of this work elaborates the importance of the image denoising and what is the necessity of image denoising in transmitted images in image enhancement topic among the final year project students as well as an emerging research topic among the research scholars worldwide. Hence this article is prepared in order to trigger those students' knowledge in basic steps involved in image processing techniques in detail. This study highly focuses on how wavelet transform is involved in denoising and impact of denoising in an image. In general, the basic transforms like Fourier transform and discrete cosine transforms are used to transform the image from one domain to another domain. Further it does not require a priori knowledge of noisy image over a transmission time. Hence the proposed method can provide a satisfactory improvement in image enhancement, assured reduction in root mean square error and improvement in the peak signal-to-noise ratio. The peak signal-to-noise ratio value of noisy image to denoised images is improved more in different wavelet transform methods. Thus, this

show abstract

A denoising approach via wavelet domain diffusion and image domain diffusion

Zhang

2016

Multimed Tools Appl

View full text Add to dashboard Cite

An edge-preserving adaptive image denoising

Cited by 11 publications

References 31 publications

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Image Denoising with Recurrent Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Edge-Preserving Segmentation Based on OAMNHA

Wavelet transform approach for image processing – A research motivation for engineering graduates

A denoising approach via wavelet domain diffusion and image domain diffusion

Contact Info

Product

Resources

About