A Study on Development of Optimal Noise Filter Algorithm for Laser Vision System in GMA Welding

To reduce image noise, we propose a novel image filter based on stepwise ratio grey model (SGM). The basic theory and the method of stepwise ratio grey prediction model are introduced first. The new filter makes use of neighborhoods around each noisy pixel to predict its intensity value and reflects the dynamics of stepwise ratio. The experimental results show that the proposed method, compared with the median filter and GM(1,1) model, improves the effect of the removal of impulse noise, such as salt & pepper noise. The improved algorithm can effectively eliminate image noise, preserve the image's details and edges, increase SNR(signal-to-noise ratio) as well as PSNR (peak signal-to-noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect. Therefore the proposed method is practicable.

The assessment of ripeness is a major part of quality evaluation and depends on several factors such as soluble solid content (SSC), acidity and firmness. Most of the methods THAT measure these qualities are destructive. So it is essential to develop an efficient and nondestructive method for measuring internal attributes of fruit. The objective of this study was to investigate visible and near infrared (Vis/NIR) spectroscopy method for prediction of SSC and pH of kiwi fruit. Near infrared spectroscopy is a fast and a nondestructive analytical technique. One of the main advantages of NIR spectroscopy is that IT allows several constituents to be measured at the same time. Transmittance determinations in the 400-1000 nm range were carried out on samples which WERE separated randomly into two groups: first group for making calibration models and second one for quality predictions. Different data preprocessing and spectra treatments such as standard normal variate transformation (SNV), multiplicative scatter correction (MSC), median filter and derivative were used to eliminate noise. Then calibration models were developed by using principal component analysis (PCA) and partial least squares (PLS). Performance of different models was assessed in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. The correlation coefficient and root mean square errors of prediction to soluble solids content and pH were 0.93, 0.952 and 0.259˚Brix, 0.236, respectively. The results indicated the feasibility of Vis/NIR transmittance spectral analysis to predict SSC and pH of kiwi fruit in a nondestructive way. Keywords: internal quality evaluation of fruit, kiwi fruit, Visible/NIR spectroscopy and nondestructive measurements

Pulmonary X-ray is a medical diagnostic method used to produce internal lung images. However, the X-ray process is often interrupted when capturing images, resulting in noisy image results. This condition diminishes the clarity of information contained in the lung X-ray images. Therefore, noise removal or denoising is essential. Denoising is a fundamental image processing technique aimed at improving image quality for optimal information transmission. This study applies denoising methods to 20 datasets of pulmonary X-ray images using Median, Mean, Gaussian, Bilateral, and Wiener filters, with Python and the OpenCV Library. Error measurement for noise filtering is conducted using Peak Signal-to-Noise Ratio and Mean Square Error methods. The research results show that the median filter stands out as an excellent denoising method, outperforming others with a Peak Signal-to-Noise Ratio of 37.6444 and a Mean Square Error of 11.3339 for Salt and Pepper Noise. Keywords: Denoising; Filtering; MSE; PSNR; X-Ray.

The concept of orthometric heights system determination plays a major key role in geodesy, and it has broad applications in various fields and activities. In geodesy, one significant quantity is the orthometric height, the height above or below the geoid along the gravity plumbline. Conventionally, the orthometric height is determined by gravimetry and levelling techniques. However, the aforementioned techniques has its own demerits. Thus, the error is accumulated with the increase of the propagation measurement line, it is difficult to convert two separated points which is located in two continents or islands separated by sea. These techniques are tedious, time consuming and expensive. In order to resolve this challenge, many researchers resort to various techniques and approaches of obtaining orthometric heights for an area using various mathematical models. It is in this quest that, this study seek to estimate orthometric heights of a mine by utilizing plausible alternative techniques based on artificial neural networks (ANN), multivariate adaptive regression splines (MARS), polynomial regression models and multiple linear regression (MLR). The working efficiency and performance of each model has been assessed based on statistical indicators of Mean (M), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Standard Deviation (SD), Correlation coefficient (R), Correction of determination (R2), and Signal to Noise Ratio (SNR). The statistical findings reveal that all the models produce satisfactory results in estimating the orthometric heights in the mine. MARS and ANN models compare to the MLR and polynomial models achieved higher results in terms of accuracy with mean and standard deviation of -0.000001888 m, +2.24736 m, and +0.005835 m and 0.095063 m respectively. This study will create the opportunity for geospatial practitioners to recognize the significant of ANN, MARS, MLR, and Polynomial model in solving some of the problems in geoscientific community.

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%).

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

According to the characteristics of the signal of the pumping unit, the processing result of the wavelet packet threshold de-drying algorithm is not good, so this paper proposes a new method to improve the algorithm. Experiments show that the improved algorithm improves the signal-to-noise ratio by 2.2 dB compared to the traditional algorithm, and the root mean square error decreases. Then the signal of the pumping unit is (CWT), the time-frequency diagram is obtained, and it is displayed in the form of grayscale image. Then the time-frequency diagram is input as the feature map, and the CNN classifier model is established to realize the intelligent fault of the pumping unit. diagnosis. The results show that the method can effectively identify the fault type of the pumping unit and has a high recognition rate.

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

Wiener filter is widely used for image denoising and restoration. It is alternatively known as the minimum mean square error filter or the least square error filter, since the objective function used in Wiener filter is an age-old benchmark called the Mean Square Error (MSE). Wiener filter tries to approximate the degraded image so that its objective function is optimized. Although MSE is considered to be a robust measurement metric to assess the closeness between two images, recent studies show that MSE can sometimes be misleading whereas the Structural Similarity (SSIM) can be an acceptable alternative. In spite of having this misleading natured objective function, Wiener filter is being heavily used as a fundamental component in many image denoising and restoration algorithms such as in current state-of-the-art of image denoising- BM3D. In this study, we explored the problem with the objective function of Wiener filter. We then improved the Wiener filter by optimizing it for SSIM. Our proposed method is tested using the standard performance evaluation methods. Experimental results show that the proposed SSIM optimized Wiener filter can achieve significantly better denoising (and restoration) as compared to its original MSE optimized counterpart. Finally, we discussed the potentials of using our improved Wiener filter inside BM3D in order to eventually improve BM3D’s denoising performance.

Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise may occur during image acquisition, transmission or storage Noise should be removed in such a way that important information of image should be preserved. We can use so many algorithms for getting the original image, by removing salt and pepper noise from the corrupted images. In this paper an algorithm is proposed for the restoration of gray scale images that are highly corrupted by impulse noise (salt and pepper noise). There are two phases in the proposed algorithm. First phase detects whether the processing pixel is corrupted or not. In the Second phase it recreates the corrupted pixel by means of the proposed algorithm. This algorithm shows better results than the Standard Median Filter (MF), Center Weighed Median Filter(CWM), Adaptive Median Filter(AMF), Adaptive Center Weighed Median Filter (ACWM), Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetrical Trimmed Median Filter Algorithm(MDBUTMF). Obtained results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less Computational time and works well in removing salt and pepper noise at low, medium and high noise densities.

Image quality, which deteriorates primarily due to noise and distortions, is critical for the effectiveness of magnetic resonance imaging (MRI) analysis. This distortion is caused because of poor imaging settings or transmission system interference. MRI scans are typically corrupted by Rician noise when acquisition takes place. This noise should be eliminated from MRI as a pre-processing phase. This paper proposes MRI denoising using deep neural network (DNN). To extract image features from noised image, the model employs a collection of convolutions. The model too uses an encoder-decoder arrangement to preserve important image features whereas rejecting irrelevant features. To reduce noise in MRI, this paper proposed DNN based technique and this technique was compared with various denoising techniques such as median filter, gaussian filter, average filter, nonlocal means filter, bilateral filter, wavelet smoothed filters. These approaches were compared using measures like peak signal to noise ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE). The results demonstrates that the suggested model can efficiently denoise MRI scans while preserving essential image features.

In medical image processing, noise removal is the challenging task. Removal of noise using the existing methods like Median Filter (MF), Center Weighted Median Filter (CWMF), Rank Condition Rank Selection Filter (RCRSF) and SMF Selective Median Filter (SMF) provides satisfactory results. In this paper, we have made improvements in wavelet shrinkage (WSRK) by providing the adaptive thresholding concept and called as Modified WRSK (MWRSK). Mean square error (MSE) and mean absolute error (MAE) has been calculated and the performance are compared. The performance of MWRSK is better than the other methods and it is also implemented in field programmable gate array (FPGA). We found this method works good for the noise removal in mammograms for the detection of breast cancer. Furthermore, the method has been tested for the sample image mdb002 taken from mammographic image analysis society (MIAS) and the results found are good.

Currently, image steganography methods are becoming popular in the field of data authentication and image processing. This provides efficient way of communication between sender and receiver without any loss in the originality of the cover image. So, the present paper proposes a novel method called as pattern based image steganography. The proposed method allows the sender to embed the secret message into hierarchical divided sub sections. During the first stage, the input cover image is divided into 25×25 non overlapped windows. Then further each window is divided into 5×5 sub sections. Then among them, the sub sections will be selected based on the pattern `Z'. Next, in the second stage, within each selected 5×5 sub section, the pixels will be selected based on the pattern `a'. Finally, in the selected pixels, 1 bit Least Significant Method (LSB) is used for embedding the message. The proposed method is experimented with nine cover images and the performance of the proposed method is measured with several measures viz., Mean Square Error(MSE), Mean Absolute Error(MAE), Root Mean Square Error(RMSE), Peak Signal to Noise Ratio(PSNR), Signal to Noise Ratio(SNR). The results show the effeciency of the proposed method.

An autocorrelation-less single-bit Weiner filter on FPGA