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Digital images are often corrupted by Impulse noise due to errors generated in noisy sensor, errors that occur in the process of converting signals from analog-to-digital and also errors that are generated in the communication channels. This error that occurs inevitably alters some of the pixels intensity while some of the pixels remain unchanged....
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Impulse noise frequently taints digital images as a result of errors produced by noisy sensors, faults during the analog-to-digital conversion process, and errors produced in communication channels. This persistent bug modifies the brightness of some pixels while keeping the brightness of other pixels unaltered. We looked at the Median Filter, Weig...
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Images and image sequences are frequently corrupted by noise in the acquisition and transmission phases. The noise added to the original image by changes the intensity of some pixels while other remains unchanged. In this paper, an Image Restoration approach has been adopted in order to of obtaining the original image from the degraded image with n...
Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In thi...
Citations
... In this regard, some researchers have studied a lot of nonlinear filter algorithms. For example, Kalman filters [4][5], median filters [6][7], sliding window filters [8][9]. The sliding mode variable structure control theory has received widespread attention from scholars due to its strong robustness and simple implementation [10][11][12]. ...
This paper proposes a novel sliding mode filter, which improves Emaru et al.’s filter. Compared to previous filters, the proposed filter has a faster response speed without sacrificing filtering performance, and the output signals do not experience chattering. Specifically, the proposed filter introduces feedforward compensation as an acceleration factor, enabling the system state to accelerate convergence without affecting the noise attenuation ability. Secondly, the discrete-time algorithm of the proposed filter is achieved through the implicit Euler discretization method, and an equivalent substitution of a sign function and a saturation function, and its output signals do not generate system chattering, which is a challenge faced by sliding mode technology in discrete-time implementation. The effectiveness of the proposed filter was determined through numerical simulation.
... The median filter [20] is a nonlinear filter that replaces the central pixel with the median of the pixels under the kernel area. The central element is always replaced by one of the pixels under kernel area. ...
Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
... single pixel in an area that is not representative at all will not have a significant impact on the median value. For this reason, the median filter is far better than the other filter in preserving sharp edges These filters are used as smoothers and useful for removing high density noise [4]. ...
In recent years the application of digital images has increased in a rapid manner, but due to noise the limited applications are currently openly adopting these applications. The noise can degrade the image quality and the application’s quality of service too. However, in literature, there are a number of different kinds of noise available and to rectify different types of noise different image filtering techniques are also available. But most of the techniques are computationally expensive or less effective, less efficient, and not able to preserve the image features for higher levels of noise. Therefore, in this paper, we introduced an optimization technique for impulse noise removal and measured its effect on the different levels of noise in the image. The proposed filter detects and removes impulse noise from digital grey-scale images. Thus the algorithm first classifies the image pixels in terms of noisy and non-noisy pixels. Here the classification of pixels has been carried out using the regression analysis of the image vector. After locating the corrupted pixel the mean of self and neighbor pixels which are non-noisy (except pixel values 0 and 255) has been used to replace the noisy pixel. However, this technique is not completely removing the noise in a single step thus we eliminate the noise in an iterative manner. Additionally to deal with the blurring effect and to preserve the image edges we employ L0 smoothing. Finally, in the last step, we utilize the median filter for constructing the final output image. The simulation of the proposed algorithm has been carried out with MATLAB and with the help of a publically available dataset. The experiments have been carried out and performance is measured in terms of the visual quality histogram and PSNR (pick signal to noise ratio). The comparison with the relevant techniques demonstrates the effective denoising consequences of the proposed technique.
... G value represents the original noisy image, F value represents the filtered image. 35 Image segmentation. In the segmentation section of the image processing technique, the desired and/or target region in an image is separated from its surrounding area by applying various operations. ...
Objectives: In this study, we aimed to trace the 2D growth development of tumoroids produced with MIA PaCa-2 pancreatic cancer cells at different time points. Methods We cultured 3 different tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations and calculated the growth rate of the tumoroids with their images acquired at 9 imaging time points by mini-Opto tomography imaging system applying image processing techniques. We used the metrics contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) to analyze the distinguishability of the tumoroid structure from its surroundings, quantitatively. Additionally, we calculated the increase of the radius, the perimeter, and the area of 3 tumoroids over a time period. Results In the quantitative assessment, the bilateral and Gaussian filters gave the highest CNR values (ie, Gaussian filter: at each of 9 imaging time points in range of 1.715 to 15.142 for image set-1). The median filter gave the highest values in PSNR in the range of 43.108 to 47.904 for image set-2 and gave the lowest values in MSE in the range of 0.604 to 2.599 for image set-3. The areas of tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations were 1.014 mm2, 1.047 mm2, and 0.530 mm2 in the imaging time point-1 and 33.535 mm2, 4.538 mm2, and 2.017 mm2 in the imaging time point-9. The tumoroids with 0.5%, 0.8%, and 1.5% agarose concentrations grew up to times of 33.07, 4.33, and 3.80 in area size over this period, respectively. Conclusions The growth rate and the widest borders of the different tumoroids in a time interval could be detected automatically and successfully. This study that combines the image processing techniques with mini-Opto tomography imaging system ensured significant results in observing the tumoroid's growth rate and enlarging border over time, which is very critical to provide an emerging methodology in vitro cancer studies.
... An image or signal's noise can be removed using the median filter (MF), a non-linear digital filtering method. Such a pre-processing procedure to reduce noise and enhances the outcomes of processing [13]. ...
... Applying a filtering technique can be a solution to avoid these issues. Hence, a median filter [20] is applied to the entire dataset to eliminate the impulse noise from the images. The mathematical expression of the median filter is defined in Eq. (3). ...
Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield. Distributing fertiliser in optimum amounts will protect the environment's condition and human health risks. Early identification also prevents the disease's occurrence in groundnut crops. A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitrogen nutrient deficiency through image features. Since chlorophyll and nitrogen are proportionate to one another, the Smart Nutrient Deficiency Prediction System (SNDP) is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be known. The model's first part is to perform preprocessing using Groundnut Leaf Image Preprocessing (GLIP). Then, in the second part, feature extraction using a convolution process with Non-negative ReLU (CNNR) is done, and then, in the third part, the extracted features are flattened and given to the dense layer (DL) layer. Next, the Maximum Margin clas-sifier (MMC) is deployed and takes the input from DL for the classification process to find CCR. The dataset used in this work has no visible symptoms of a deficiency with three categories: low level (LL), beginning stage of low level (BSLL), and appropriate level (AL). This model could help to predict nitrogen deficiency before perceivable symptoms. The performance of the implemented model is analysed and compared with ImageNet pre-trained models. The result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99% and 95%, respectively, compared to existing pre-trained models.
... Median filtering may be a technique of image enhancement within the spatial domain. This methodology is enclosed within the class of non-linear filtering [13]. The workings of the algorithmic program are median filtering every pixel output value is decided by the median of setting mask are determined, the pixel values are taken within the variety of spatial window with a minimum size of 3x3 then the present values are sorted in ascending [14]. ...
... The Given image is denoted by U 0 (x). The energy function is given as: (13) where c1 and c2 are the average of u0(x) within and outside (T) severally, L 2 is the 2D Lebesgue measure of the -neighborhood of the edge set T and λ 1 and λ 2 are fitting term parameters. ...
... Applying a filtering technique can be a solution to avoid these issues. Hence, a median filter [20] is applied to the entire dataset to eliminate the impulse noise from the images. The mathematical expression of the median filter is defined in Eq. (3). ...
... Although neural networks have become a research hotspot in recent years, the datasets in neural networks are difficult to obtain, and the data training speed can be slow. The common image filtering algorithms 2 of 13 based on spatial domain include mean filter [8], median filter [9], Gaussian filter [10], and bilateral filter [11]. However, these algorithms can only remove specific noise effectively, so they are not suitable for speckle removal in laser images. ...
... Although neural networks have become a research hotspot in recent years, the datasets in neural networks are difficult to obtain, and the data training speed can be slow. The common image filtering algorithms based on spatial domain include mean filter [8], median filter [9], Gaussian filter [10], and bilateral filter [11]. However, these algorithms can only remove specific noise effectively, so they are not suitable for speckle removal in laser images. ...
Laser speckle noise caused by coherence between lasers greatly influences the produced image. In order to suppress the effect of laser speckles on images, in this paper we set up a combination of a laser-structured light module and an infrared camera to acquire laser images, and propose an improved weighted non-local mean (IW-NLM) filtering method that adopts an SSI-based adaptive h-solving method to select the optimal h in the weight function. The analysis shows that the algorithm not only denoises the laser image but also smooths pixel jumps in the image, while preserving the image details. The experimental results show that compared with the original laser image, the equivalent number of looks (ENL) index of the IW-NLM filtered image improved by 0.80%. The speckle suppression index (SSI) of local images dropped from 4.69 to 2.55%. Compared with non-local mean filtering algorithms, the algorithm proposed in this paper is an improvement and provides more accurate data support for subsequent image processing analysis.
... Boateng et al. [14] proposed other criteria to judge if a pixel is noisy or normal. Compared to the standard median filter, this method performs well essentially in Salt-and-pepper noise. ...
... Figures 2, 3 and 4 show the result after adding 20%, 50% and 80% noise density. Algo1, Algo2, Algo3, Algo4, Algo5 and Algo6 are respectively the improvements of [11][12][13][14][15][16]. The picture quality obtained from the application of our improved median filter algorithm on MRI with different noise levels shows that the proposed filter is superior in terms of filtering Salt and pepper noise. ...
