Mejora De La Imagen De Satélite: Enfoque Sistemático Para Reducción De Ruido Y Mejora De Resolución

Rasti, Pejman; Tasmaz, Haci; Daneshmand, Morteza; Özçinar, Çağrı; Anbarjafari, Gholamreza

doi:10.6036/7676

Cited by 3 publications

(3 citation statements)

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“…Different authors have given their different approaches to make enhancement in color images on domains by using different area of specialization like artificial intelligence, neural networks vice versa. They perform a comparative papers which analysis a fundus image enhancement techniques to discover diabetic retinopathy (DR) (Rasti et al, 2016). The comparative performance and evaluation of these several enhancement techniques will supporting the selecting best and appropriate technique that may much develop the detection of diabetic retinopathy.…”

Section: Related Workmentioning

confidence: 99%

A comparative Study of Image Enhancement Techniques for Natural Images

Ismael

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.

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Section: Related Workmentioning

confidence: 99%

A comparative Study of Image Enhancement Techniques for Natural Images

Ismael

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…Local mean filter, median filter, and wavelet threshold are tended to remove noise using de-noising image in wavelet and spatial domain. [6] adaptive wavelet thresholding MSE=24.9 [10] thresholding denoising in wavelet domain adaptive Wiener filter PSNR 30.76 MSE=54.57 [8] Emerging wavelet domain Denoising methods such as soft and hard thresholding, bayeshrink, visushrink and SUREshrink PSNR = 37.529605 MSE=11.484705 [9] (Gaussian , Bilateral filter, Bayes Shrink, SURENeighShrink) PSNR =30.910 [11] wavelet domain based on the generalized Guassian distribution (GGD), NormalShrink PSNR =4% [12] (Bayes Shrink, Sure shrink, Bivariate shrink, Block Shrink) PSNR =68.59 [13] Non-local means filters and its method noise thresholding using wavelets PSNR =35.60 [14] Hard thresholding + median filter PSNR=34.07, MSE=21.22 [15] Mf PSNR= 74.2921, MSE= 0.0024 [16] spatial domain BF and hybrid thresholding function in the wavelet domain PSNR=23.5499 MSE=23.0438 [17] mean filter +WT PSNR=26.6476 [18] satellite image enhancement system consisting of denoising and resolution enhancement PSNR=33.09 [19] WT, BF, GF and BFWT PSNR=34.76 [1] MF + DWT PSNR=26.5469…”

Section: Introductionmentioning

confidence: 99%

Image denoising using wavelet thresholding and median filter based Raspberry pi

2021

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The goal of any de-noising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner otherwise artefacts can be introduced which may blur the image. In this work, three levels of Gaussian noise are used for adding noise on the original image (σ=10, σ=50, σ=100) and also (σ=15, σ=20, σ=25) to compare with Ramadhan et al.[1] and analysis with it to test embedded system with median filter. Performance evaluation of the median filter, wavelet threshold de-noising techniques is provided. The techniques used are namely the median filter and wavelet threshold is used to remove noise based on raspberry pi with Python. Four methods to remove noise image are used. MF, WT, MF before and after WT. The results showed the image of camera was better than other after tested all the methods with Gaussian noise σ=10. On other hand the other images were better than image of camera for the Gaussian level 50 and 100. The results were good in median filter in wavelet threshold based on Raspberry Pi, which is compared with overall result most of images butter in median filter. KEYWORDSImage Denoising Median Filter Wavelet Threshold Gaussian Noise This is an open-access article under the CC-BY-SA license This research devided in severap part. Section II explain the m methodology includes Literature Review, addition noise model, spatial domain filtering, Wavelet transform, Wavelet threshold, and the parameters. The results willo be delivered in Section III, includes Image Denoising, comparison benchmark. The last section (IV) shows the conclusion.

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“…It is relatively recent that wavelet analysis was used for specific aim in economics and finance. The recent literature relating to its application can be seen in Gençay et al (2010), Bogdanova (2015), Benhmad (2013), Rasti (2016), Alzahrani, Masih, Wu (2014) and Al-Titi (2014) among the others.…”

Section: Wavelet Decompositionmentioning

confidence: 99%