Universal Image Quality Index Research Articles

Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images.

This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitting problem. The volume data were rotated and translated with random intensity and frequency, in three dimensions, and were iterated as the number of slices in the volume data. Then, for every slice, a portion of the motion-free k-space data was replaced with motion k-space data, respectively. In addition, based on the transposed k-space data, we acquired MR images with motion artifacts and residual maps and constructed datasets. For a quantitative evaluation, the root mean square error (RMSE), peak signal-to-noise ratio (PSNR), coefficient of correlation (CC), and universal image quality index (UQI) were measured. The U-Net models for motion artifact reduction with the residual map-based dataset showed the best performance across all evaluation factors. In particular, the RMSE, PSNR, CC, and UQI improved by approximately 5.35×, 1.51×, 1.12×, and 1.01×, respectively, and the U-Net model with the residual map-based dataset was compared with the direct images. In conclusion, our simulation-based dataset demonstrates that U-Net models can be effectively trained for motion artifact reduction.

Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study.

In order to evaluate the effects of forest fires on the dynamics of the function and structure of ecosystems, it is necessary to determine burned forest areas with high accuracy, effectively, economically, and practically using satellite images. Extraction of burned forest areas utilizing high-resolution satellite images and image classification algorithms and assessing the successfulness of varied classification algorithms has become a prominent research field. This study aims to indicate on the capability of the deep learning-based Stacked Autoencoders method for the burned forest areas mapping from Sentinel-2 satellite images. The Stacked Autoencoders, used in this study as an unsupervised learning method, were compared qualitatively and quantitatively with frequently used supervised learning algorithms (k-Nearest Neighbors (k-NN), Subspaced k-NN, Support Vector Machines, Random Forest, Bagged Decision Tree, Naive Bayes, Linear Discriminant Analysis) on two distinct burnt forest zones. By selecting burned forest zones with contrasting structural characteristics from one another, an objective assessment was achieved. Manually digitized burned areas from Sentinel-2 satellite images were utilized for accuracy assessment. For comparison, different classification performance and quality metrics (Overall Accuracy, Mean Squared Error, Correlation Coefficient, Structural Similarity Index Measure, Peak Signal-to-Noise Ratio, Universal Image Quality Index, and KAPPA metrics) were used. In addition, whether the Stacked Autoencoders method produces consistent results was examined through boxplots. In terms of both quantitative and qualitative analysis, the Stacked Autoencoders method showed the highest accuracy values.

Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering

Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.
 To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

AbstractIn the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

A conventional and non-conventional analysis of SAR image despeckling technique

The importance of high-resolution Synthetic Aperture Radar (SAR) imaging is undeniable in applications for Earth monitoring since it offers useful data for analysis. Post-image processing methods including edge and object detection, segmentation, and speckle noise removal are important to performed to find the information of images. The key technique that makes images aesthetically pleasing and understandable is despeckling. The presented study assays the latest trends & state of the art techniques for SAR image despeckling to gauge the performance & agility of existing techniques by performing a contemporary combination of theoretical & experimental analysis. This study analyses various techniques used in literature. The quantitative & qualitative analysis is done using Peak Signal-to-Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) indices to find the better approach

A new fast and efficient dehazing and defogging algorithm for single remote sensing images

Information about the earth’s surface is difficult to capture in remote sensing images because bad weather greatly curtails visibility and diminishes visual contrast in the images. For the purpose of military survey and aerial surveillance, these images are crucial for providing information. It is quintessential to eradicate bad weather conditions like haze and fog from remote sensing images. Recently, numerous deweathering initiatives and endeavors have been undertaken to alleviate these limitations. These contemporary deweathering approaches, however, are inadequate to recover dense haze images. This work presented a novel visibility restoration approach based on segmentation and unsharp mask guided filtering method. It consists of the following steps to restore the scene’s radiance: First, a segmentation method is employed to determine atmospheric light to quantify contrast and color. Estimation of the transmission map utilizing the dark channel prior is then performed to precisely determine the proximity between objects. The resultant output image contains halo artifacts and inconsistencies in the structure. A guided filter method based on unsharp masking is employed to optimize the transmission map in order to solve this issue. The experimental results demonstrate that the proposed indicators ensures high uniformity regarding qualitative and quantitative evaluation using six performance metrics: mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), universal image quality index (UQI), fog-aware density evaluator (FADE), dehazing algorithm index (DHQI), standard deviation (CC), and blind contrast enhancement assessment (e and r).

Robust Image Steganography Based on Hybrid Edge Detection

Steganography is a technique for hiding digital messages into other digital media, such as images, audio, video, and others so that the messages can be disguised and not directly visible to the human vision. There are two kinds of steganography by domain, i.e., spatial domain and frequency domain [7]. Least Significant Bit (LSB) is a popular method in spatial domains [8]. LSB is not a new method. However, this method has many advantages, such as simple algorithm and the quality of imperceptibility of relatively good stego-image [6, 9]. This makes the LSB still open for more development and further research. This paper presents the image hiding technique using Least Significant Bit (LSB) steganography based on hybrid edge detection that maximized the data embedding capacity of the cover image. The performance of the system is evaluated for hybrid edge detection using pairs of various edge detection techniques such as Canny, Kirsch, Sobel, and Prewitt. The results of the proposed steganography approach are estimated for different payload capacities based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), and Universal Image Quality Index (UQI). It is examined that the proposed approach performs better than the existing state of arts for image steganography.

Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data.

SLAAHE: Selective Apex Adaptive Histogram Equalization

Medical imaging enables specialists to make diagnoses our internal body organs for detecting anomalies without being intrusive. Unfortunately, the images produced by various modalities are not only plagued by noises, but the images contrasts are also subpar, making it impossible to use them for a delicate task like diagnosis, which is directly concerned with our health. Pre-processing the image becomes obligatory to ameliorate its medical worth and contrast enhancement is a sacrosanct part of diagnostic image pre-processing. Even though some popular contrast enhancement algorithms, such as Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), etc., are widely used, they are insufficient for some medical imaging modalities, such as ultrasound imaging (USG). This paper, named as Selective Apex Adaptive Histogram Equalization (SLAAHE), proposes an effective and efficient contrast enhancement algorithm that dynamically selects the best window size for dividing the low contrast image into image grids and clip limit for clipping the histogram of the low contrast sub-image only. Unique feature of the proposed algorithm is that it clips the image histogram only when it is not well-distributed and ennobles the output image contrast. This makes the algorithm best concerning the contrast enhancement quality, and reduced time and space complexity. Both theoretical analysis and assessment of experimental results with statistical metrics like Universal Image Quality Index (UIQI), Absolute Mean Brightness Error (AMBE), Multi-Scale Structural Similarity Index Measure (MSSSIM), Entropy, and Root Mean Square Error (RMSE) exhibit how the proposed method has gained momentous improvement over existing state-of-the-art methods. For the evaluation metrics UIQI, AMBE, MSSSIM, Entropy, and RMSE, the suggested technique received average scores of 0.79, 0.12, 0.88, 0.92, and 11.52 respectively.

Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN

In integrated circuit manufacturing, defects in epoxy drops for die attachments are required to be identified during production. Modern identification techniques based on vision-based deep neural networks require the availability of a very large number of defect and non-defect epoxy drop images. In practice, however, very few defective epoxy drop images are available. This paper presents a generative adversarial network solution to generate synthesized defective epoxy drop images as a data augmentation approach so that vision-based deep neural networks can be trained or tested using such images. More specifically, the so-called CycleGAN variation of the generative adversarial network is used by enhancing its cycle consistency loss function with two other loss functions consisting of learned perceptual image patch similarity (LPIPS) and a structural similarity index metric (SSIM). The results obtained indicate that when using the enhanced loss function, the quality of synthesized defective epoxy drop images are improved by 59%, 12%, and 131% for the metrics of the peak signal-to-noise ratio (PSNR), universal image quality index (UQI), and visual information fidelity (VIF), respectively, compared to the CycleGAN standard loss function. A typical image classifier is used to show the improvement in the identification outcome when using the synthesized images generated by the developed data augmentation approach.

Using feature maps to unpack the CNN ‘Black box’ theory with two medical datasets of different modality

Convolutional neural networks (CNNs) have been established for a comprehensive range of computer vision problems across several benchmarks. Visualization and analysis of feature maps generated by convolutional layers can be an effective approach to explore the hidden and complex characteristic of a CNN model. Convolutional layers provide diverse feature maps however, the extent of this diversity needs to be explored. This research attempts to provide five insights of the ‘Black box’ mechanism of CNNs, using skin cancer dermoscopy and lung scan computed tomography (CT) Scan datasets by statistically analyzing layer by layer (three convolutional layers) feature maps using 17 geometrical and 6 intensity-based features to determine the characteristics and level of diversity. Significance and difference of the feature maps layer by layer, black feature maps analysis, difference of the feature maps to each other and to the original image, variations among the feature maps when running the model multiple times and inter-class variation among the feature maps for different iteration are explored. Various statistical methods including T-test, analysis of variance (ANOVA), mean, median, mean squared error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), root mean squared error (RMSE), dice similarity score (DSC), universal image quality index (UQI) and Spectral angle mapper (SAM) are employed. Experimental results show that for the skin cancer dermoscopy dataset, a large number of black feature maps are produced (20–60%) while the proportion of black feature maps for the CT Scan dataset is comparatively low (2–20%). This demonstrates that for different datasets, feature maps with diverse characteristics can be produced. The layer by layer differences between the feature maps is evaluated using T-tests and ANOVA for seventeen geometrical features and six intensity-based features. For both datasets across most of the geometrical features and across most of the intensity-based features a significant diversity can be observed. The difference of the feature maps to each other and to the original image is quite high, with MSE values for the dermoscopy and CT Scan datasets in the range of 1860–31,399 and 171–6089, respectively, PSNR 3–15 and 10–25, SSIM values of 0.01–0.84 and 0.3–0.81, RMSE values of 0.81–1 and 0.21–1, DSC values of 0.37–0.53 and 0.47–0.75, UQI values of 0.02–0.86 and 0.01–0.88 and SAM values of 0.12–1.53 and 0.19–1.55 for the dermoscopy and CT Scan datasets respectively. When running the model multiple times (three iterations), a notable iteration by iteration diversity is found in terms of mean, median, maximum and minimum values for most of the geometrical features. The inter-class variation among the feature maps for different iterations and layers are evaluated based on the F-value of the ANOVA test. For the dermoscopy dataset, the highest mean F-value is found for layer 1 and iteration 3 while for the CT scan dataset the highest mean F-value is found for layer 3 and iteration 3 indicating that for these feature maps the highest inter-class dissimilarity is generated. The findings of this study may aid in exploring the complex mechanism of convolutional layers, kernels and feature maps.

Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

Endoscopy is a widely practiced technique across the globe, utilized to envision the gastrointestinal (GI) tract of the human body without causing harm. In GI endoscopy, images affected by noise may not be helpful to support a visual diagnosis of the mucosal surface at good quality. In such advanced imaging systems, image quality enhancement becomes particularly beneficial. In this study, a non-invasive numerical method of image enhancement is examined to improve the image quality metrics as is critical in GI imaging. A unique approach of weighting and thresholding is examined in this study, where the weighting and thresholding play a significant role in image enhancement. Deviations from the proposed weighting scheme results in the reduction of the peak signal to noise ratio (PSNR) among other key metrics. The proposed method evaluated on eight datasets are expounded to produce optimal performance, particularly in that of the PSNR, entropy and contrast over the state-of-the-art (SOA) enhancement algorithms. The proposed method yields a universal image quality index of 93 %, a structural similarity of 86 % and that of 25 %, 11 %, 19 % and 30 % of PSNR, entropy, contrast, and distortion metric enhancements on average in comparison with the SOA, contributing to the improved quality of the resultant frame aiding to view image features at higher detail and overall quality, both quantitively and qualitatively. This in conjunction with higher optical magnification and faster graphics processing units (GPU’s) may be beneficial for a real-time examination of the mucosal surface of the GI tract at superior quality.

Pixel-Level Feature Extraction Model for Breast Cancer Detection

Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not. However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them. Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA). The spatial features of the images were extracted using various filters, and the most prevalent ones are selected using the GA and fed into the classifiers for pixel-level categorization. Three classifiers—random forest (RF), decision tree (DT), and extra tree (ET)—were used in the proposed model. The parameters of all models were separately tuned, and their performance was tested. The results show that the features extracted by using the GA+PCA in the proposed model are influential and reliable for pixel-level classification in service of the image annotation and tumor identification. Further, an image from benign, malignant, and normal classes was randomly selected and used to test the proposed model. The proposed model GA-PCA-DT has delivered accuracies between 0.99 to 1.0 on a reduced feature set. The predicted pixel sets were also compared with their respective ground-truth values to assess the overall performance of the method on two metrics—the universal image quality index (UIQI) and the structural similarity index (SSI). Both quality measures delivered excellent results.

Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)

Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), visual information fidelity (VIF), Frechet Inception Distance (FID), and translation accuracy. The experimental results indicate that the synthetic images generated either by the Cycle GAN or by the Ad CycleGAN have lower MSE and RMSE, and higher scores in PSNR, UIQI, and VIF in homogenous image translation (i.e., Y → Y) compared to the heterogenous image translation process (i.e., X → Y). The synthetic images by Ad CycleGAN through the heterogeneous image translation have significantly higher FID score compared to Cycle GAN (p < 0.01). The image translation accuracy of Ad CycleGAN is higher than that of Cycle GAN when normal images are converted to COVID-19 positive images (p < 0.01). Therefore, we conclude that the Ad CycleGAN with the independent criterion can improve the accuracy of GAN image translation. The new architecture has more control on image synthesis and can help address the common class imbalance issue in machine learning methods and artificial intelligence applications with medical images.

Digital Image Anti-Forensic Model Using Exponential Chaotic Biogeography-Based Optimization Algorithm

Abstract The innovation in visual imagery has led to massive growth in technologies, wherein digital cameras are obtainable at affordable prices. Thus, the digital images are easily captured and processed due to the internet connectivity. On the other hand, the development of strong image editing software facilitated the forgers to manipulate the accessible images with different tampering operations. Several techniques are devised for detecting the forgeries. Accordingly, this paper devises an anti-forensic model, namely Exponentially Weighted Moving Average-Chaotic Biography Based Optimization (E-CBBO) for joint photographic experts group (JPEG) compression to mitigate the forgeries occurred on the internet while transmitting data. The proposed E-CBBO is designed by integrating the properties of the exponentially weighted moving average (EWMA) with the chaotic biography-based optimization (CBBO). The suggested JPEG anti-forensic model is used to eliminate JPEG compression artifacts through the use of unique deblocking, smoothing with dither and decalibration operations. In addition, the goal is to balance visual quality and forensic undetectability when compressing the JPEG image. The fitness function is developed using the structural similarity index (SSIM), universal image quality index (UIQI) and histogram deviation parameters. With a maximum accuracy of 93.2%, a minimal MSE of 0.110, a maximum SSIM of 0.932 and a maximum UIQI of 0.890, the suggested E-CBBO beat existing approaches.

A hybrid approach for enhancement of abdominal CT images

In this technological era, digital imaging is integrated into every aspect of our life, and it has a predominant and essential role in medical investigations. Different types of diseases are being diagnosed using Computed Tomography (CT) images, acquired with varying bit depths, and kept in conventional Digital Imaging and Communications in Medicine (DICOM) format. However, one of the key challenges faced by the practitioners experimenting in the field of biomedical image analysis is that the images acquired are of low contrast. This research work aims to propose a method centered on the windowing technique and anisotropic diffusion filter to enhance the contrast of medical images for further analysis. The suggested approach does adjustments of contrast and brightness using the information extracted from the DICOM header of considered abdominal CT images to efficiently visualize the details for further processing. Furthermore, we have analyzed and compared its performance with distinct notable techniques which have been typically used for the enhancement of the medical image. The recorded outcomes revealed that the suggested algorithm gives better results with average values of Peak Signal to Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE), and Feature Similarity (FSIM) index equivalent to 27.71 dB, 8.38, and 0.96, respectively. Further, the average value of the Universal Image Quality (UIQ) index and Edge Content (EC) is equivalent to 0.83 and 9.37, respectively. Based on the experimental results performed on 15,000 images of a real human CT dataset, we have observed that the suggested algorithm gives much better results from subjective and objective perspectives.

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images.

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.

Classification of fired cartridge cases using 3D image capture and a comparison of database correlation method performance.

When firearms are used, they leave unique marks on the fired bullets and shells. Examining these marks gives clues about the crime scene and the weapon of the crime. However, due to the increase in the number of incidents in which firearms are used, the increase in the amount of evidence makes ballistic investigations very difficult and prolongs the analysis time of the evidence. With the automatic image analysis and identification system, ballistic evidence can be examined very quickly and used to classify possible matching evidences. In this study, we present an approach based on Image Similarity Measurement for forensic examination of cartridges from automatic pistols. For this purpose, we used 500 images of 9 × 19 mm and 500 images of 7.65 × 17 mm cartridge cases obtained from 20 different brands and models of pistols. We divided the images of bullet cartridges into four different categories as breech face, firing pin, ejector mark, and combined evaluation, and we obtained these images from the BALİSTİKA 2010 system. We investigated the classification and matching performance of the obtained four category images using 4 different methods: Structural Similarity (SSIM), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Universal Image Quality Index (UQI). The results show that our Structural Similarity (SSIM) approach is effective in classifying and matching ballistic evidence.

Luminosity Control and Contrast Enhancement of Digital Mammograms Using Combined Application of Adaptive Gamma Correction and DWT-SVD

Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.