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The digitalized image enhancement methods offer multiple options to improve the visual quality of images. The histopathological image assessment is the golden standard to diagnose endometrial cancer, which is also called uterine cancer that seriously affects the reproductive system of females. Owing to the limited capability, complex relationship among histopathological images and its elucidation utilizing existing methods frequently fails to obtain satisfying outcomes. As a result, in this study, the Pelican crow search optimization_multiple identities representation network (PCSO_MIRNet) is presented for improving the quality of histopathology images of uterine tissue. First, the histopathological images are given to pre-processing stage, which is performed by the median filter. The image enhancement is done utilizing MIRNet, which is trained by devised PCSO. The PCSO is developed by incorporating Pelican Optimization Algorithm (POA) and Crow Search Algorithm (CSA). Furthermore, PCSO_MIRNet attained the best outcomes with a maximal peak signal-to-noise ratio (PSNR) of 44.741 dB, minimal mean squared error (MSE) of 0.937, and minimal degree of distortion (DD) value achieved is 0.068 dB.

Face recognition in surveillance video depends on the surveillance camera and the surrounding environment. The problem becomes more complex to identify human faces in such surveillance videos when the frames have low resolution details and are affected by various noises. While several researchers have attempted to remove noise from degraded video frame images, there remains a void in the research of denoising surveillance images. It is observed that the existing denoising algorithms are not giving satisfactory results. Hence, we propose a Multistage Multifilter (MSMF) algorithm with four stages which employs super pixel segmentation followed by three different denoising techniques, namely Principal Component Analysis (PCA), Median, and anisotropic diffusion filtering, to denoise the low-resolution and noisy surveillance images. Overall, this approach to denoise surveillance images involves a combination of statistical, nonlinear, and edge-preserving filtering techniques to address the challenges posed by the noise present in these low-resolution images. The results are quite promising with 59.12% as the average Peak Signal to Noise Ratio (PSNR) value for Gaussian Noise and 60.15% as the average PSNR value for Salt and Pepper Noise.

A robust and reliable implicit method is proposed for application in data interpolation. The algorithm is based on a recently developed analytic approximation method, namely the distributed approximating functionals (DAFs), which is known to have the "well-tempered" property of UNIFORMLY approximating a function and its derivatives. In comparison with the conventionally used local explicit interpolation algorithms, the implicit method achieves much more accurate interpolation results because it couples all sample values (both known and unknown) in the domain of interest using a set of simultaneous linear algebraic equations. Due to the fact that the well-tempered DAFs also are very good low-pass filters, the performance of the DAF-based implicit method is not affected very much by the high frequency noise in the input signal. As an application, the proposed algorithm is applied to signals that are corrupted with impulse noise.

The timely and precise discovery of traffic signs is considered an effective part of modeling automated vehicle driving. However, the dimension of traffic signs accounted for a lower ratio of input pictures which elevated the complexity of discovery. Hence, a new model is devised using faster region-based convolution neural network (faster R-CNN) traffic for detecting traffic signs. The Region of Interest (RoI) extraction and Median filter are executed for discarding the superfluous data from the dataset. The method extracted a Pyramid Histogram of Oriented Gradient (PHoG), local vector pattern (LVP), CNN and ResNet features for generating beneficial information. It is used to lessen the loss of contextual data and the data augmentation is further applied for making the training of the model more stable and time-saving. The traffic sign recognition is executed with faster R-CNN wherein the tuning of hyperparameters such as batch normalization rate, epoch and learning rate is determined by the proposed pelican cuckoo search (PCS). The method revealed improved efficacy without presenting additional computational costs in the model. Moreover, the faster R-CNN is termed the finest technique to enhance the discovery of traffic signs. The proposed PCS-based faster R-CNN outperformed with the highest precision 92.7%, specificity of 93.7% and $F$-measure of 93.2%.

The article proposes an adaptive, noise-exclusive and assessment-based fuzzy switching median filter for high intensity impulse noise. The filter adaptively changes the size of sliding window in accordance with noise intensity and noise-exclusive operation is performed only on noise-free pixels. These steps thus preserve the image details effectively. Judicious assessment of the previously restored pixels is made in order to use them in replacing the noisy pixels and further improve the efficiency of filter. With these possibilities, fuzzy switching median filter-based approach is also incorporated for improving the performance. Experimental results justified the efficacy of the proposed fuzzy-based filtering method qualitatively and quantitatively for twelve different highly corrupted noisy images, and showed significant improvement to existing algorithms.

The restoration process of cracked images is a challenge and an important field of image and video processing. Old images that experienced bad treatment and environmental conditions, or images of old buildings and statues have the problem of cracks. This problem restricts the ability of extracting information and processing of the image. Many algorithms have been proposed to restore cracked images but most of them failed to remove these cracks efficiently based on realistic assumptions. So, we developed and implemented a new algorithm in order to repair cracked images efficiently by proposing new techniques such as seam processing, stochastic analysis and learning process. The basic motivation of this work is to design a simple algorithm of efficient computation complexity and memory usage that can be used in an interactive fashion where a simple set of parameters is used to control behavior and performance of the algorithm. The algorithm uses seam processing to discover cracks and local spatial information to compensate and handle information shortage based on statistical analysis and data generation. In general, the algorithm can be divided into three phases: cracks detection, cracks filling, and post-processing phase to enhance the quality. The results show how the algorithm deals with two cracked images of extreme deterioration. The results are based on subjective tests where 85 persons graded and evaluated the results under different conditions through three separate sessions.

This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.

Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models.

Recently, many number of people various countries contracted the COVID-19 (C-19) disease. The identification of this harmful disease and other pneumonia diseases is an important process in the medical world. CT-Scan is mainly prescribed to predict the severity of lung diseases affected by C-19 and other pneumonia diseases. Generally, a CT-Scan image is represented as a grayscale image (GI). Biomedical-oriented GIs are more complex and they are too difficult to recognize the status of lung diseases directly from the experimental images. To address this type of medical problem, a multifractal approach can be applied to analyze and illustrate the GIs in detail. Therefore, the multifractal dimensional analysis is used to diagnose and explore the vehemence of the contamination levels in the lungs. In this study, the complexity of CT-Scan images of C-19 patients is analyzed in order to perform a comparison in terms of age and noise levels. It was also discovered that the intricate of CT-Scan images is considerably varied for patients aged 50 and above when compared to younger subjects. This comparative study indicates that the deadly virus affects elderly persons compared to youngsters. The proposed age-based classification is supported by image processing techniques, qualitative measures, and statistical tools. The obtained results are demonstrated graphically by Generalized Fractal Dimensions (GFD) spectrum, 3D visualization, ANOVA table, and box plots to expose the rate of discrimination of complexity in CT-Scan lung images.

The retina is the most important part of the eye. By proper feature extraction, it can be the first step to detect a disease. Morphology of retina blood vessels can be used to identify and classify a disease. A step, such as segmentation and analysis of retinal blood vessels, can assist medical personnel in detecting the severity of a disease. In this paper, vascular segmentation using U-net architecture in the Convolutional Neural Network (CNN) method is proposed to train a sematic segmentation model in retinal blood vessel. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used to increase the contrast of the grayscale and Median Filter is used to obtain better image quality. Data augmentation is also used to maximize the number of datasets owned to make more. The proposed method allows for easier implementation. In this study, the dataset used was STARE with the result of accuracy, sensitivity, specificity, precision, and F1-score that reached 97.64%, 78.18%, 99.20%, 88.77%, and 82.91%.

In this paper, a method of electronic conductivity measurement is presented. It combines two well known methods of electrochemistry: cyclic voltammetry and chronoamperometry. This DC technique uses the Hebb–Wagner approach to block ionic conduction (when steady state conditions are reached) and allows electronic conduction of solid electrolytes to be determined. In order to get short diffusion times, a micro contact is used as an ion blocking electrode. However, as the electronic conduction in electrolytes is and should be very low, the current is also very low, typically some tens of nanoamps. Thus, the heating system inevitably generates noise problems that are solved using a median filter. As opposed to other related work, our system allows the determination of the conductivities without any preliminary smoothing or fitting of the curves (since the noise is strongly reduced). Some results with oxygen ion conductors are also given.

Image Processing are still being challenged by noises. Noise causes the intensity manipulation of the image. So, removing or reducing the noises from the image is a must before working with it. It is an active area of research because none of the established or proposed noise reducing methods can return back the original image. And also, there are different types of noises. Different proposed algorithms work fine with different types of noises and also up to a certain level of noises. In this paper, an adaptive noise removal algorithm is proposed which works fine with impulse noises and does not blur the edges of the inputted image. While removing the noises, the algorithm uses an adaptive mask which is n × n square or cross musk, n is usually an odd number. Our proposed algorithm has achieved 15.38 dB (Peak Signal to Noise Ratio) outperforming the existing filters.

A new method of detecting optical sight’s fault is presented based on computer techniques of image processing and pattern recognition theory. The presented technique can detect the fault of the optical sight including dark spots, bright spot, flash and dimmer with high efficiency and precision, as well as, get rid of the traditional estimation model relying on the human eye observation. Image acquisition system, image processing system and image fault recognizing system is mainly discussed in this paper. Using the technique of noise filtering and background noise separation, we successfully realized the division of image fault and background to obtain feature of optical sight fault. On the whole the detection method can carry out the recognition of fault of optical sight effectively.

The paper focuses on the research and application of neural network for feature extraction from the three dimensional radar echo data, proposed a method of combining the median filter and Kohonen neural network to resolve it. Simulation results show that the proposed method in the paper is feasible, the results is more stable and more accurate, apparently has an advantage over the ordinary self-organization neural network. To validate this method by applied to two groups of real radar data set. Result show that the proposed method is practical with ninety percent accuracy.