Gaussian noise reduction in digital images using a modified fuzzy filter

Rahman, Tanzila; Haque, Mohammad Reduanul; Rozario, Liton Jude; Uddin, Mohammad Shorif

doi:10.1109/iccitechn.2014.7073143

Cited by 20 publications

(6 citation statements)

References 10 publications

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“…Sara Parrolli and others [8] has proposed a novel despeckling algorithm for (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage to reduce additive Gaussian noise from the SAR images in the year 2012. Tanzila Rehman and others in 2014 [9] used the modified fuzzy filter to reduce Gaussian noise and preserve details of Image simulating with zero mean and 0.01 to 0.05 variance value. Athira K. Vijay1, M. Mathurakani in 2014 [10], had introduced Dual-Tree Complex Wavelet Transform (DT-CWT) along with Byes thresholding.…”

Section: Related Workmentioning

confidence: 99%

Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

Shaikh¹,

Badroddin²

2021

IRJASH

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The microwave remote sensing system is the broadest tool use to get information about the earth's objects in the form of images. These images are very much affected by various noises, which affects the precision of the images. These, in turn, affect the information present in it. To improve, the quality of satellite images, denoising of the image is an essential task. Gaussian noise majorly seen in all remotely sensed images. Natural sources cause this noise during the acquisition of the image. Many Researchers used Numerous types of filtering techniques to reduce such noises. In this paper, we have focused on lowering Gaussian noise from the microwave satellite dataset. To minimise Gaussian noise, the spatial domain Gaussian Low Pass Filter (GLPF) with different window sizes applied to a single Polarized (HH) L-band ALOS PALSAR satellite SLC level-1 Datasets. The results of filtered images are evaluated based on the respective mean, standard deviation and coefficient of variance.

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Section: Related Workmentioning

confidence: 99%

Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

Shaikh¹,

Badroddin²

2021

IRJASH

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“…These two types of distortions usually occur in the industrial setting. Generally, the wood images are exposed to Gaussian white noise due to the poor illumination and heat in the lumber mill while acquiring the wood images [8,27].…”

Section: Wood Imagesmentioning

confidence: 99%

A No-Reference Image Quality Assessment Metric for Wood Images

Rajagopal¹,

Mokhtar²,

Khairuddin³

et al. 2021

JRNAL

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Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA metric is proposed to assess the quality of wood images. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood images.

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“…The ten reference wood images were then distorted by Gaussian white noise and motion blur to represent the image distortions, which were encountered in the industrial setting. To be specific, Gaussian white noise often arises during the acquisition of wood images due to the sensor noise [22] caused by poor illumination and high ambient temperature in the lumber mill [8]. Meanwhile, wood images were subjected to motion blur upon the presence of relative motion between the camera and the wood slice [6].…”

Section: Training and Testing Databasementioning

confidence: 99%

No-reference quality assessment for image-based assessment of economically important tropical woods

et al. 2020

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Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.

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Gaussian noise reduction in digital images using a modified fuzzy filter

Cited by 20 publications

References 10 publications

Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

A No-Reference Image Quality Assessment Metric for Wood Images

No-reference quality assessment for image-based assessment of economically important tropical woods

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