Search

The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-contrast images, edge retention is the most crucial aspect to consider. This helps in the efficient retrieval of information. This paper provides a two-step edge-preserving homomorphic SAR image despeckling technique that implements a guided filter as the first step, and a modified method of noise thresholding using the bivariate shrinkage rule and canny edge operator in the Discrete Orthonormal Stockwell Transform (DOST) domain as the second step. The use of a canny edge operator improves overall edge preservation after despeckling. The use of noise thresholding delivers the highest level of speckle reduction in the DOST domain. The detected edges are added to the residual part obtained after removing the noise to produce more informative content. According to several qualitative and quantitative criteria, the suggested approach is compared to some of the newest despeckling methods. The execution time of the proposed method is around 7.2679 seconds. Upon conducting qualitative and quantitative analysis, it has been determined that the proposed method surpasses all other despeckling methods that were compared.

Ultrasound is a diagnostic imaging technique to detect various illnesses related to the body's internal organs. It is a non-invasive, safe, cost-effective, and simple imaging modality that uses high-frequency sound waves and their reflection from internal organs. However, a key drawback of using ultrasound imaging tools is that they are often contaminated by multiplicative speckle noise, which can reduce the image resolution and contrast and complicates the diagnosis of the tiny structure of lesions. Extensive studies have shown that eliminating speckle noise is a necessary pre-processing task for computer-assisted ultrasound image systems. This paper proposes a novel method for denoising, weighted thresholding, and fusion of ultrasound images in the Non-Subsampled Contourlet Transformation (NSCT) domain. The method utilizes (a) The Bayesian and Bivariate shrinkage rule in the NSCT domain, (b) A new local structural similarity-fusion-based homomorphic ultrasound image despeckling method in the NSCT domain, and (c) A weights estimation approach based on a bilateral method that helps improve noise suppression and edge preservation results. The performance of the proposed algorithm is evaluated in terms of mean squared error (MSE), peak signal-to-noise ratio, structural similarity index (SSIM), speckle suppression index, signal-to-MSE ratio, and speckle signal-to-noise ratio. Extensive computer simulation results show that the presented method performs better in subjective visual inspection than existing state-of-the-art despeckling techniques.

This study looks at how API (Application Programming Interface) wrapper technology can make it easier to use complex functions by putting together a lot of API calls. These packages have non-real-time interfaces that are hard to use. ChatGPT is a chatbot-specific GPT-3 language paradigm. It lets developers create chatbots that respond intelligently to natural language user input, creating a more engaging user experience. This article shows that ChatGPT, Python, and API wrapper technology may be used to develop a smart chatbot. We show how to use the OpenAI API library to add ChatGPT to Python programs. This makes it easier for developers to make chatbots that sound and act more like real people when they talk. Our contribution to this field is showing that it is possible to make smart chatbots with ChatGPT and API wrapper technology. To reach this goal, we use a system that combines the OpenAI API with ChatGPT and Python. This gives us valuable information about how to make smart chatbots. The efficiency of the system has been tested many times while applying it to different environments, and the results are satisfactory.

AI is a contemporary methodology rooted in the field of computer science [...]

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.

Different soil characteristics in different parts of India affect agriculture growth. Crop growth and crop production are significantly impacted by healthy soil. Soil enzymes mediate almost all biochemical reactions in the soil. Understanding the biological processes of soil carbon and nitrogen cycling requires defining the significance of prospective elements at the play of soil enzymes and evaluating their activities. A combination of Multiple Linear Regression (MLR), Random Forest (RF) models, and Artificial Neural Networks (ANN) was employed in this study to assess soil enzyme activity, including amylase and urease activity, soil physical properties, such as sand, silt, clay, and soil chemical properties, including organic matter (SOM), nitrogen (N), phosphorus (P), soil organic carbon (SOC), pH, and fertility level. Compared to other methods for estimating soil phosphatase, cellulose, and urease activity, the RF model significantly outperforms the MLR model. In addition, due to its ability to manage dynamic and hierarchical relationships between enzyme activities, the RF model outperforms other models in evaluating soil enzyme activity. This study collected 3972 soil samples from 25 villages in the Bhandara district of Maharashtra, India, with chemical, physical, and biological parameters. Overall, 99% accuracy was achieved for cellulase enzyme activity and 94% for N-acetyl-glucosaminidase enzyme activity using the Random Forest model. Crops have been suggested based on the best performance accuracy algorithms and evaluation performance metrics.

This paper presents a new technique for despeckling of Synthetic Aperture Radar (SAR) images using a local correlation based fusion of high-frequency coefficients in Discrete Wavelet Transform (DWT) with method noise thresholding. The decomposition level is decided by analyzing the texture of the input image at each level by calculating entropy. The core idea of the proposed technique lies in the selection of decomposition level in 2D-DWT based on entropy parameter and on the fusion of high-frequency coefficients. On decomposition, the low-frequency coefficients remain untouched and the high-frequency coefficients are thresholded using two different shrinkage rules. Therefore the Bayesian and Bivariate shrinkage methods are applied to the high-frequency coefficients. After performing two different thresholding methods, the improved high-frequency coefficients are fused using local correlation based strategy. The threshold value is calculated by correlation strategy. Later the correlation coefficient (CC) is evaluated between the two improved high-frequency coefficients. The CC is now compared with the threshold value for the fusion purpose. On the basis of defined fusion strategy, the average and maximum operation are applied to perform the fusion of high-frequency coefficients. The despeckling scheme is followed by method noise thresholding in order to preserve the fine details of the image. The performance of the proposed method is assessed using metrics such as Signal-to-Noise Ratio (SNR), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM) and visual appearance of the despeckled image. The experimental results demonstrate the effectiveness of proposed work over prior works on SAR image despeckling.

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion.

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones.

In synthetic aperture radar (SAR) images, degradation due to multiplicative speckle noise and detail blurring is one of the common problem. This problem is solved by despeckling method. The main objective of image despeckling is to eliminate the speckle noise and preserve the important details of SAR images such as texture, edges, structures and corners. Usually SAR images are high dimensional images and preserving the edge and corner components is one major issue. Anisotropic diffusion also called Perona-Malik diffusion is used to reduce noise without disturbing the significant parts of the image. An homomorphic scheme is proposed using anisotropic diffusion in db2-type wavelet transform. Linear and non-linear filters are used on the approximate part of the image to remove blurring. Method noise thresholding is used to restore the unfiltered part of the despeckled image. The proposed method is applied and tested on correlated speckle noise as well as uncorrelated speckle noise on the real dataset of SAR images. The performance of the proposed method is evaluated by its visual quality and by using other metrics such as PSNR, SSIM, UIQI and RMSE. The performance and computational time are calculated and compared with standard filters and methods. The critical analysis of the result shows that proposed method gives the brilliant outcome in terms of structure, edge preservation and noise suppression. The proposed method has the ability to be used in practical applications. Keywords: Image despeckling, Wavelet transform, Anisotropic diffusion, Method noise

The removal of granular pattern multiplicative speckle noise is the major issue in Synthetic Aperture Radar (SAR) images. Theoretically, multiplicative noise is considered as the ratio of the standard deviation to the signal value. The proposed scheme works on db2 based 2D-Discrete Wavelet Transform (DWT) using wavelet thresholding and method noise. The scheme is designed to despeckle the simulated SAR images and real SAR images. It uses a hybrid combination of Directional Smoothing Filter (DSF), wavelet thresholding using enhanced Bayesian shrinkage rule and method noise for despeckling. After DWT decomposition, the approximate component is directed to DSF followed by method noise thresholding and detailed components are subjected to wavelet thresholding followed by method noise thresholding. The article justifies the efficient use of method noise and explains how its exact use can enhance the result of the algorithm over other efficient methods. The quality assessment of the proposed method is done by visual appearance and measures such as Peak Signal-to-Noise Ratio (PSNR), Structural similarity index metric (SSIM), Universal Image Quality Index (UIQI), Equivalent Numbers of Looks (ENL), Noise Variance (NV), Coefficient of Variation (CV), Mean Squared Error (MSE), Correlation Coefficient (CC) and preservation of mean values before and after despeckling. The effectiveness of the proposed method is demonstrated by matching it to well-known speckle noise removal methods on SAR images. The proposed method has the capability to be used in real time practical applications. Keywords: SAR image, Speckle noise, DWT, Directional smoothing filter, Method noise