A New Method for Adaptive Median Filtering of Images

Lyakhov, Pavel; Orazaev, A.R.; Chervyakov, Nikolay I.; Kaplun, Dmitry

doi:10.1109/eiconrus.2019.8657050

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…In their work, an approach focused on the AMF has been suggested to minimize the negative effect resulting from conventional procedures by adopting iterative image processing with small-masks and pre-processing of the median filter outcomes and improved the efficiency of the processing. The numerical assessment of simulation results by PSNR and SSIM concluded that the suggested approach achieved better clears images from low-intensity and highintensity impulse noise via an intensity of (90-99) percent and it helps solve the impulse noise problem more efficiently [39].…”

Section: Spatial Domain Filteringmentioning

confidence: 98%

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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Section: Spatial Domain Filteringmentioning

confidence: 98%

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

Ismael¹,

Baykara²

2021

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“…The Digital images are indicated by a set of pixels that have intensity values (Lyakhov et al 2019) [31]. Considering the straightforwardness of the statement, let us consider grayscale images, that have intensity values which are encoded with 8-bit numbers and vary from 0 (black color) to 255 (white color).…”

Section: Adaptive Median Filtermentioning

confidence: 99%

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

Nirmala¹,

Gomathy²

2023

Computer Systems Science and Engineering

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In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method based on Convolutional Neural Network (CNN) is proposed for automatic pumpkin leaf image classification. The Proposed Denoising and deep Convolutional Neural Network (CNN) method enhances the Pumpkin Leaf Preprocessing and diagnosis. Real time data base was used for training and testing of the proposed work. Investigation on existing pre-trained network Alexnet and googlenet was investigated is done to evaluate the performance of the proposed method. The system and computer simulations were performed using Matlab tool.

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“…For example, Chowdhury et al, proposed a dynamic filtering algorithm [2] that eliminates Gaussian noise in digital images by using a Gaussian kernel and Gaussian function. Lyakhov et al, proposed an adaptive median filtering algorithm for reducing image pulse noise [3]. This algorithm can effectively eliminate extreme noise from images.…”

Section: Related Work 21 Image Recognitionmentioning

confidence: 99%

Stateful-Service-Based Pupil Recognition in Natural Light Environments

Horng

Chen

et al. 2022

Healthcare

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Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.

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A New Method for Adaptive Median Filtering of Images

Cited by 15 publications

References 9 publications

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

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

Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

Stateful-Service-Based Pupil Recognition in Natural Light Environments

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