• Title/Summary/Keyword: Denoising

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An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising

  • Lin, Lin
    • Journal of Information Processing Systems
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    • v.14 no.2
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    • pp.539-551
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    • 2018
  • Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

A REVIEW ON DENOISING

  • Jung, Yoon Mo
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.18 no.2
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    • pp.143-156
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    • 2014
  • This paper aims to give a quick view on denoising without comprehensive details. Denoising can be understood as removing unwanted parts in signals and images. Noise incorporates intrinsic random fluctuations in the data. Since noise is ubiquitous, denoising methods and models are diverse. Starting from what noise means, we briefly discuss a denoising model as maximum a posteriori estimation and relate it with a variational form or energy model. After that we present a few major branches in image and signal processing; filtering, shrinkage or thresholding, regularization and data adapted methods, although it may not be a general way of classifying denoising methods.

Translation-invariant Wavelet Denoising Method Based on a New Thresholding Function for Underwater Acoustic Measurement (수중 음향 측정을 위한 새로운 임계치 함수에 의한 TI 웨이블렛 잡음제거 기법)

  • Choi, Jae-Yong
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.16 no.11
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    • pp.1149-1157
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    • 2006
  • Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

Wiener Filter Based Denoising Algorithm for Demosaicking (디모자이킹을 위한 Wiener Filter 기반의 디노이징 알고리듬)

  • Lee, Rok-Kyu;Jeong, Je-Chang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.5C
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    • pp.286-294
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    • 2011
  • In most digital cameras, images are obtained by a sensor overlaid by the color filter array (CFA) such as Bayer, demanding a demosaicking procedure to rebuild the full resolution color images. However, due to the nature of sensor, it is necessary to consider denoising step to remove the noise. In this paper, we analyze demosaicking and denoising jointly and show that the proposed method can solve the denoising issue by simple manner, well suppress different level of noises. The proposed algorithm yields comparable performances measured by several image quality assessment (CPSNR, SCIELAB, and FSIM), while the computational cost is low.

Gamma spectrum denoising method based on improved wavelet threshold

  • Xie, Bo;Xiong, Zhangqiang;Wang, Zhijian;Zhang, Lijiao;Zhang, Dazhou;Li, Fusheng
    • Nuclear Engineering and Technology
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    • v.52 no.8
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    • pp.1771-1776
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    • 2020
  • Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

Wavelet-based Image Denoising with Optimal Filter

  • Lee, Yong-Hwan;Rhee, Sang-Burm
    • Journal of Information Processing Systems
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    • v.1 no.1
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    • pp.32-35
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    • 2005
  • Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.

A Performance of Positioning Accuracy Improvement Scheme using Wavelet Denoising Filter (Wavelet Denoising Filter를 이용한 측위 정밀도 향상 기법 성능)

  • Shin, Dong Soo;Park, Ji Ho;Park, Young Sik;Hwang, Yu Min;Kim, Jin Young
    • Journal of Satellite, Information and Communications
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    • v.9 no.3
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    • pp.9-14
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    • 2014
  • Recently, precision guided munition systems and missile defense systems based on GPS have been taking a key role in modern warfare. In warfare however, unexpected interferences cause by large/small scale fading, radio frequency interferences, etc. These interferences result in a severe GPS positioning error, which could occur late supports and friendly fires. To solve the problems, this paper proposes an interference mitigation positioning method by adopting a wavelet denoising filter algorithm. The algorithm is applied to a GPS/QZSS/Wi-Fi combined positioning system which was performed by this laboratory. Experimental results of this paper are based on a real field test data of a GPS/QZSS/Wi-Fi combined positioning system and a simulation data of a wavelet denoising filter algorithm. At the end, the simulation result demonstrates its superiority by showing a 21.6% improved result in comparison to a conventional GPS system.

Real-Time Non-Local Means Image Denoising Algorithm Based on Local Binary Descriptor

  • Yu, Hancheng;Li, Aiting
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.2
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    • pp.825-836
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    • 2016
  • In this paper, a speed-up technique for the non-local means (NLM) image denoising method based on local binary descriptor (LBD) is proposed. In the NLM, most of the computation time is spent on searching for non-local similar patches in the search window. The local binary descriptor which represents the structure of patch as binary strings is employed to speed up the search process in the NLM. The descriptor allows for a fast and accurate preselection of non-local similar patches by bitwise operations. Using this approach, a tradeoff between time-saving and noise removal can be obtained. Simulations exhibit that despite being principally constructed for speed, the proposed algorithm outperforms in terms of denoising quality as well. Furthermore, a parallel implementation on GPU brings NLM-LBD to real-time image denoising.

Design and Implementation of a Boundary Matching System Supporting Partial Denoising for Large Image Databases

  • Kim, Bum-Soo;Kim, Jin-Uk
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.5
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    • pp.35-40
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    • 2019
  • In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.

Deep Network for Detail Enhancement in Image Denoising (영상 잡음 제거에서의 디테일 향상을 위한 심층 신경망)

  • Kim, Sung Jun;Jung, Yong Ju
    • Journal of Korea Multimedia Society
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    • v.22 no.6
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    • pp.646-654
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    • 2019
  • Image denoising is considered as a key factor for capturing high-quality photos in digital cameras. Thus far, several image denoising methods have been proposed in the past decade. In addition, previous studies either relied on deep learning-based approaches or used the hand-crafted filters. Unfortunately, the previous method mostly emphasized on image denoising regardless of preserving or recovering the detail information in result images. This study proposes an detail extraction network to estimate detail information from a noisy input image. Moreover, the extracted detail information is utilized to enhance the final denoised image. Experimental results demonstrate that the proposed method can outperform the existing works by a subjective measurement.