De-noising of Gaussian noise affected images by Non-Local Means algorithm
20 Mar 2013-pp 1215-1218
TL;DR: Noise removal and image enhancement are the important tasks addressed by many Image Processing algorithms, especially, when the images are corrupted by high noise level e.g. night shoots, where there exists a need for efficient image de-noising method without introducing any artifacts in the original image.
Abstract: Noise removal and image enhancement are the important tasks addressed by many Image Processing algorithms, especially, when the images are corrupted by high noise level e.g. in the case of remote imaging, thermal imaging, night vision etc. The noise makes the image recognition more difficult as it gives a grainy, snowy or textured appearance to the image. So there exists a need for efficient image de-noising method without introducing any artifacts in the original image. The Images with a Noise Standard Deviation (Sigma) greater than 25 are considered as high noise images. The dark images, e.g. night shoots, have very low dynamic range of brightness. The darkness and the high noise needs to be carefully tackled by the image processing algorithm for acceptable visual quality e.g. surveillance applications. Furthermore, de-noising is often necessary as a pre-processing step in image compression, segmentation, recognition etc. Basically, the image de-noising methods are divided into two types: local and non-local. A non local method called as Non-Local Means [4] estimates a noise-free pixel intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighbourhood of the pixel being processed and local neighbourhoods of surrounding pixels. The method is quite spontaneous that results in PSNR and visual quality comparable with other de-noising methods.
Citations
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01 Jan 2020
TL;DR: In this paper, modern algorithms of detection, classification and tracking of UAVs by using optical flow will be reviewed and the most promising methods comes from radiolocation and computer vision areas.
Abstract: Lately unmanned aerial vehicles (UAV) became an unseparated part of modern life. These machines gained their particular popularity in the field of amateur or professional video production and small cargo deliveries. But they are not only limited by civilian lines of activity. Nowadays they are also used in military conflicts either as scout or as a deadly precise weapon. Due to UAV’s easy market access, these drones can be used by the hands of terrorists groups. It’s not a rocket science to attach an explosive device to UAV, guide it to strategic area and detonate it from far away. This is one of the main danger sources for such places like nuclear power plants. A downward trend also led to new type of thread – swarm attacks, when group of small UAVs strikes their victim. A timely detection of such attacks is not a trivial task. This question is being studied by many researches from different fields. The most promising methods comes from radiolocation and computer vision areas. Next in the paper, modern algorithms of detection, classification and tracking of UAVs by using optical flow will be reviewed. Usually, the common approach for this task comes from classic computer vision methods or, highly popular today, deep learning methods.
5 citations
Cites methods from "De-noising of Gaussian noise affect..."
...Currently the de-facto standard are methods like adaptive median filtering [1], bilateral filtering [2], non-local means filtering [3] and, surprisingly, Gaussian blur filter due to it low computational cost....
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TL;DR: The improved preclassification non local-means (IPNLM) is proposed for filtering grayscale images degraded with additive white Gaussian noise (AWGN) and reveals good results outperforming other filters based on NL-mean by balancing the tradeoff between the noise suppression, detail preservation, and processing time.
Abstract: In this paper, we develop an extensive research on different types of grayscale images applying standard non local (NL)-means algorithm on different search and patch windows sizes to obtain optimal parameters where the values of criteria peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and structural similarity index (SSIM) would be the best possible. The research shows quantitatively the importance on the appropriate selection of the windows sizes used during the filtering process. Based on the optimal parameters of the standard NL-means, we propose the improved preclassification non local-means (IPNLM) for filtering grayscale images degraded with additive white Gaussian noise (AWGN). The proposal uses a descriptors evaluation for each search window in the noisy image to apply statistical neighborhood preclassification respect to the homogeneity of each window to distinguish whether the current noisy pixel is in a homogeneous region or it is in an edge object region. Also, two thresholds based on the standard deviation of the local region in the noisy image are proposed to classify the pixels and perform a filtering level degree providing a commitment between the image denoising and the processing time. The proposal IPNLM reveals good results outperforming other filters based on NL-means by balancing the tradeoff between the noise suppression, detail preservation, and processing time. Experimental results demonstrate that IPNLM algorithm can reduce considerably the processing time from 8 through 15 times in comparison with the standard NL-means and other analyzed filters.
3 citations
01 Jan 2015
TL;DR: This paper has examined and compared the results of the Modified Haar Denoising algorithm using Laplacian pyramid and Adaptive segmentation with the other existing denoising methods and tells that PSNR of proposed algorithm is higher than the otherexisting denoised methods and RMSE of proposed algorithms is lesser than the others.
Abstract: 3 ABSTRACT: Image denoising is the first and much necessary step to execute before examining an image. Problems with the data acquisitive process, Imperfectness of instruments, and interferingness natural phenomena are the main reasons to spoil an image. The goal of denoising is to take out the utmost noise from an image while keeping as much as possible the important data, signal features. In this paper we have examined and compared the results of the Modified Haar Denoising algorithm using Laplacian pyramid and Adaptive segmentation with the other existing denoising methods. The noise affected image is denoised by Modified Haar DWT method using Laplacian pyramid and Adaptive thresholding. The comparison of the denoisng methods are done by using performance parameter Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) between the pilot and noise affected image and PSNR, RMSE between pilot and reconstructed (denoised) image. The results tells that PSNR of proposed algorithm is higher than the other existing denoising methods and RMSE of proposed algorithm is lesser than the other existing denoising methods. As a result the denoised image will be visually appealing after reconstruction. For removing the noise from the image MATLAB is used to implement the proposed method in this paper.
01 Jan 2014
TL;DR: In this paper, a new technique called improved non local means has been explored, which is more efficient than conventional NLM and achieves excellent performance in digital image processing.
Abstract: Noise removal and image enhancement are the important tasks addressed by many Image Processing algorithms, especially, when the images are corrupted by high noise level e.g. in the case of remote imaging, thermal imaging, night vision etc. Image denoising has been a well studied problem in the field of image processing. Denoising technique is a pre-processing step in compression, segmentation and restoration. Denoising is classified into two types: Local and Non local means. The presence of similar patterns and features in an image are referred to as Non Local means. Non local means algorithm assumes that the image contains excessive redundancy and these redundancies can be used to remove the noise present in the image. It estimates noise-free pixel intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighborhoods. The recently proposed non local means achieves excellent performance in digital image processing. In addition to the conventional non local means, a new technique called improved non local means has been explored. By using pre- classification, similar block searching and weighted averaging, the INLM filtering is more efficient than conventional NLM.
References
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20 Jun 2005
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
Abstract: We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.
6,804 citations
"De-noising of Gaussian noise affect..." refers background or methods in this paper
...As a remedy on the loss of image details after noise filtering, Baudes A., Coll B., Morel I.M. have developed the Non-Local Means (NL-Means) algorithm [4]....
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...Ga is a Gaussian kernel and h is a filtering parameter [4]....
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TL;DR: The performance of this method for removing noise from digital images substantially surpasses that of previously published methods, both visually and in terms of mean squared error.
Abstract: We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.
2,439 citations
01 Oct 2000
TL;DR: The authors describe various discrete transforms and their applications in different disciplines and demonstrate their power and practicality in data compression.
Abstract: From the Publisher:
The Transform and Data Compression Handbook serves as a handbook for a wide range of researchers and engineers." "The authors describe various discrete transforms and their applications in different disciplines. They cover techniques, such as adaptive quantization and entropy coding, that result in significant reduction in bit rates when applied to the transform coefficients. With presentations of the ideas and concepts, as well as descriptions of the algorithms, the authors provide insight into the applications and their limitations. Data compression is an essential step towards the efficient storage and transmission of information. The Transform and Data Compression Handbook provides information regarding different discrete transforms and demonstrates their power and practicality in data compression.
347 citations
"De-noising of Gaussian noise affect..." refers methods in this paper
...In this case, a locally adaptive filter can keep a track of noise variance during the NL-Means estimation process and later a local filter based on Karhunen-Loeve Transform (KLT) [1] is applied in those particular areas to remove the remaining noise and improve the PSNR....
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01 Jan 2008
TL;DR: It is shown that the NLMeans algorithm is basically the first iteration of the Jacobi optimization algorithm for robustly estimating the noise-free image and also an extension to noise reduction of coloured (correlated) noise.
Abstract: Recently, the NLMeans filter has been proposed by Buades et al. for the suppression of white Gaussian noise. This filter exploits the repetitive character of structures in an image, unlike conventional denoising algorithms, which typically operate in a local neighbourhood. Even though the method is quite intuitive and potentially very powerful, the PSNR and visual results are somewhat inferior to other recent state-of-the-art non-local algorithms, like KSVD and BM-3D. In this paper, we show that the NLMeans algorithm is basically the first iteration of the Jacobi optimization algorithm for robustly estimating the noise-free image. Based on this insight, we present additional improvements to the NLMeans algorithm and also an extension to noise reduction of coloured (correlated) noise. For white noise, PSNR results show that the proposed method is very competitive with the BM-3D method, while the visual quality of our method is better due to the lower presence of artifacts. For correlated noise on the other hand, we obtain a significant improvement in denoising performance compared to recent wavelet-based techniques.
134 citations
"De-noising of Gaussian noise affect..." refers background in this paper
...As stated by B. Goossens et al [5], in certain cases, single iteration of NL-Means may not remove the complete noise from the image....
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