Image de-noising using fuzzy and wiener filter in wavelet domain

Kethwas, Akash; Jharia, Bhavana

doi:10.1109/icecct.2015.7226071

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Wiener filters (Wiener 1949) are used primarily as a denoising technique in traditional signal processing both for vision (Kazubek 2003;Kethwas and Jharia 2015) and speech (Fingscheidt, Suhadi, and Stan 2008;Suhadi, Last, and Fingscheidt 2011;Meyer, Elshamy, and Fingscheidt 2020). These filters typically operate in the frequency domain and assume that the spectral properties of the original images as well as the noise are known or can be estimated.…”

Section: Wiener Filtermentioning

confidence: 99%

From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation

Kapoor

Bär

Varghese

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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Despite recent advancements, deep neural networks are not robust against adversarial perturbations. Many of the proposed adversarial defense approaches use computationally expensive training mechanisms that do not scale to complex real-world tasks such as semantic segmentation, and offer only marginal improvements. In addition, fundamental questions on the nature of adversarial perturbations and their relation to the network architecture are largely understudied. In this work, we study the adversarial problem from a frequency domain perspective. More specifically, we analyze discrete Fourier transform (DFT) spectra of several adversarial images and report two major findings: First, there exists a strong connection between a model architecture and the nature of adversarial perturbations that can be observed and addressed in the frequency domain. Second, the observed frequency patterns are largely image-and attack-type independent, which is important for the practical impact of any defense making use of such patterns. Motivated by these findings, we additionally propose an adversarial defense method based on the well-known Wiener filters that captures and suppresses adversarial frequencies in a data-driven manner. Our proposed method not only generalizes across unseen attacks but also beats five existing state-of-the-art methods across two models in a variety of attack settings.

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Section: Wiener Filtermentioning

confidence: 99%

From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation

Kapoor

Bär

Varghese

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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“…Simultaneously, the wavelet denoising technique eliminates image details and produces smooth image sharpness. As a result, there is a need for such an algorithm (hybrid method) in image denoising to filtering noise without affecting essential image characteristics, as well as and the elimination of mixed noise and the achievement of an image of appropriate quality with the loss of the smallest possible value of image details during the noise reduction process [13,14].…”

Section: Introductionmentioning

confidence: 99%

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

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Method for Removing Secondary Peaks in Remote Field Eddy Current Testing of Pipes

et al. 2016

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Image de-noising using fuzzy and wiener filter in wavelet domain

Cited by 7 publications

References 8 publications

From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation

From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Method for Removing Secondary Peaks in Remote Field Eddy Current Testing of Pipes

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