Unsharp Masking Technique Research Articles

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP.

Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification. To overcome the limitations of small existing datasets, Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking techniques are applied to enrich dataset images, enhancing information and improving model generalization. Addressing the limitation of the Vision Transformer's self-attention structure in capturing input token sequences, a novel relative position encoding method is employed to enhance the overall predictive capabilities of the model. Furthermore, the introduction of residual structures in the Multi-Layer Perceptron tackles convergence degradation during training, leading to faster convergence and enhanced algorithm accuracy. Finally, this study comprehensively analyzes the network model's performance on validation sets in terms of accuracy, precision, and recall. Experimental results demonstrate that the proposed model achieves a classification accuracy of 91.36% on an augmented open-source brain tumor dataset, surpassing the original VIT-B/16 accuracy by 5.54%. This validates the effectiveness of the proposed approach in brain tumor classification, offering potential reference for clinical diagnoses by medical practitioners.

A deep convolutional neural network architecture for breast mass classification using mammogram images

<p>Breast cancer is one of the second most common cancer occurring worldwide. Early identification of the disease is a major interest that promises to propose several diagnostic procedures to prevent further surgical interventions. This research paper aims to develop a breast mass classifier system using deep learning to differentiate breast mass images from normal mammographic images. The benchmark mammographic datasets CBIS-DDSM, INbreast, and mini-MIAS are used for constructing the proposed model DELU-BM-CNN. The region of interest is identified by applying image processing techniques (median filter, binarization and dilation) and the images are enhanced and sharpened using adaptive histogram equalization and unsharp masking techniques. The pre-processed images are trained with a minimum of five deep convolutional layers activated by an Exponential Linear Unit (ELU) which is developed from scratch for feature learning and classifying the given whole mammographic images. Dropout, Data normalization, and Global average pooling are some of the regularization techniques adopted to prevent the model from over-fitting. The proposed models are able to classify CBIS-DDSM images with an accuracy of 96.60%, INbreast images with 96.20% and MIAS images with 97.40%. The experimental results are also compared with conventional Rectified Linear Unit (ReLU) and Leaky ReLU activation function that promises the proposed model as a good prognosticator than the state-of-art models for cancer diagnosis using mammogram images as input.</p>

Underwater image contrast enhancement through an intensity-randomised approach incorporating a swarm intelligence technique with unsupervised dual-step fusion

ABSTRACT Analysing objects in an underwater medium is difficult due to light problems in such an environment. Low contrast and visibility in water medium result in restricted information extraction. Some previous methods inadequately reduce underwater colour cast and improve the image contrast minimally. This work proposes intensity-randomised underwater image contrast enhancement (IRUCE), an improved enhancement method that integrates an unsupervised dual-step fusion technique to reduce blue-green colour cast, improve contrast, and enhance image brightness, especially in turbid and deep underwater media. IRUCE enhances the inferior colour channels and moderates the dominant colour channel to reduce the colour cast before the intensity-randomised approach is implemented. The intensity-randomised approach is designed to effectively distribute image intensity across all intensity levels. Next, a swarm intelligence algorithm is fused to perform median equalisation on all colour channels. The median intensity values of inferior colour channels are shifted towards the dominant colour channel. The unsharp masking technique is employed in the last step to increase image sharpness. The effectiveness of this approach is validated through quantitative and qualitative evaluations, and the results are compared with those of other state-of-the-art methods. Outcomes indicate that the proposed method improves underwater image quality significantly and outperforms other contemporary techniques.

The origin of the planetary nebula M 1–16

We investigated the origin of the Planetary Nebula (PN) M 1–16 using narrow-band optical imaging, and high- and low-resolution optical spectra to perform a detailed morpho-kinematic and chemical studies. M 1–16 is revealed to be a multipolar PN that predominantly emits in [O III] in the inner part of the nebula and [N II] in the lobes. A novel spectral unsharp masking technique was applied to the position-velocity (PV) maps to reveal a set of multiple structures at the centre of M 1–16 spanning radial velocities from −40 km s−1 to 20 km s−1, with respect to the systemic velocity. The morpho-kinematic model indicates that the deprojected velocity of the lobe outflows are ≥100 km s−1, and particularly the larger lobes and knots have a deprojected velocity of ≃350 km s−1; the inner ellipsoidal component has a deprojected velocity of ≃29 km s−1. A kinematical age of ~8700 yr has been obtained from the model assuming a homologous velocity expansion law and a distance of 6.2 ± 1.9 kpc. The chemical analysis indicates that M 1–16 is a Type I PN with a central star of PN (CSPN) mass in the range of ≃0.618 – 0.713 M⊙ and an initial mass for the progenitor star between 2.0 and 3.0 M⊙ (depending on metallicity). An Teff ≃ 140 000 K and log(L/L⊙) = 2.3 was estimated using the 3MdB photoionisation models to reproduce the ionisation. stage of the PN. All of these results have led us to suggest that M 1–16 is an evolved PN, contrary to the scenario of proto-PN suggested in previous studies. We propose that the mechanism responsible for the morphology of M 1–16 is related to the binary (or multiple star) evolution scenario.

An approach for improving Optical Character Recognition using Contrast enhancement technique

Digital cameras and mobile image capture of documents are two examples of new developments in the fields of optical character recognition and text recognition. Scans of text or text photographic images and even natural photography results can be distorted to the point where OCR digitization is inaccurate. It offers a unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.

Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel

The inner-surface damage of water conveyance tunnels is the main hidden danger that threatens their safety and leads to serious accidents. The method based on the principle of acoustic reflection is the main means of inspecting damage to water-conveyance tunnels. However, affected by the tunnel environment and equipment noise, the obtained acoustic point cloud model inevitably suffers from noise, which can produce erroneous results. Therefore, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the acoustic point cloud model of water-conveyance tunnels. The proposed method fuses the ideas of guided filtering and the unsharp masking technique and extends them to the 3D point cloud model by considering the position of the point. In addition, edge-aware weighting mean is also used to retain the edge features of the point cloud model while smoothing the noise points. The experimental results show that our method can obtain impressive results and a better performance in both the acoustic point cloud model of the tunnel and the simulated point cloud model than many state-of-the-art methods.

Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants

Food production is a growing challenge with the increasing global population. To increase the yield of food production, we need to adopt new biotechnology-based fertilization techniques. Furthermore, we need to improve early prevention steps against plant disease. Guava is an essential fruit in Asian countries such as Pakistan, which is fourth in its production. Several pathological and fungal diseases attack guava plants. Furthermore, postharvest infections might result in significant output losses. A professional opinion is essential for disease analysis due to minor variances in various guava disease symptoms. Farmers’ poor usage of pesticides may result in financial losses due to incorrect diagnosis. Computer-vision-based monitoring is required with developing field guava plants. This research uses a deep convolutional neural network (DCNN)-based data enhancement using color-histogram equalization and the unsharp masking technique to identify different guava plant species. Nine angles from 360∘ were applied to increase the number of transformed plant images. These augmented data were then fed as input into state-of-the-art classification networks. The proposed method was first normalized and preprocessed. A locally collected guava disease dataset from Pakistan was used for the experimental evaluation. The proposed study uses five neural network structures, AlexNet, SqueezeNet, GoogLeNet, ResNet-50, and ResNet-101, to identify different guava plant species. The experimental results proved that ResNet-101 obtained the highest classification results, with 97.74% accuracy.

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking.

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.

Deep Auto Encoder Based Extreme Learning System for Automatic Segmentation of Cervical Cells

Nowadays, cervical cancer has emerged as one of the major causes of death and incredibly difficult to identify. In this research, three novel methods have been developed for the automatic detection and classification of cervical cancer in Pap images. There are five steps in this framework which includes data collection, preprocessing, segmentation, feature extraction, and classification of images. The proposed method is implemented using Herlev Pap smear data set. The input image from the data set is pre-processed by using an Anisotropic Diffusion Filter with Unsharp Masking technique that removes the noise in the Pap data. In the next step, an enhanced image sequence is segmented automatically by the proposed Advance Map-Based Superpixel Segmentation (AMBSS) algorithm. Finally, cervix cancer images are procured by utilizing an AMBSS and classified by Support Vector Machine classifier. The accuracy obtained is 85.4%. In order to improve the accuracy AMBSS with quasi newton-based Feed Forward Neural Network classification is used and the accuracy of 96.0% is obtained. Furthermore, AMBSS with Deep auto encoder-based Extreme Learning Machine classification is performed and achieved the accuracy of 99.1%. The findings show that the precision of classification typically exceeds other intelligent methods previously applied.

Substructure analysis of the RXCJ0232.2–4420 galaxy cluster

ABSTRACT RXCJ0232.2–4420, at z = 0.28, is a peculiar system hosting a radio halo source around the cool-core of the cluster. To investigate its formation and nature, we used archival Chandra and XMM–Newton X-ray data to study the dynamical state of the cluster and detect possible substructures in the hot gas. Its X-ray surface brightness distribution shows no clear disruption except an elongation in the north-east to south-west direction. We perform the unsharp masking technique and compute morphology parameters (Gini, M20, and concentration) to characterize the degree of disturbance in the projected X-ray emission. Both of these methods revealed a substructure, which is located at ∼1 arcmin from the cluster core in the south-west direction. Previous spectral analysis conducted for RXCJ0232.2–04420 concluded that there are a short cooling time and low entropy at the cluster centre, indicating that the cluster has a cool core. Thus, we suggest that RXCJ0232.2–4420 may be a system where the core of the cluster is not showing any sign of disturbance, but the south-west substructure could be pumping energy to the detected radio halo via turbulence.

Color correction and adaptive contrast enhancement for underwater image enhancement

To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper and lower color channels to the lower color channels. Subsequently, an adaptive contrast enhancement algorithm is applied to each color channel is proposed to produce the background-stretched and foreground-stretched images, the combination of these two images significantly improves the contrast of the output image. Finally, a unsharp masking technique is applied to sharpen the overall image. Experiments on the standard underwater image enhancement benchmark dataset have proven that our algorithm can output high-quality images, while the performer of our algorithm is at least 5% better than the second-best in the average of all metric scores.

A Novel Approach for Mammogram Enhancement using Nonlinear Unsharp Masking and L0 Gradient Minimization

Abstract Mammography is an effective modality for detecting and diagnosing breast cancer. Mammogram enhancement is used to improve the local contrast of mammograms, such that lesions appear more visible in the enhanced image. This paper introduces two novel approaches for mammogram enhancement based on a new unsharp masking scheme, called nonlinear UM and L0 Gradient Minimization, NLUML0GMIN for detecting breast cancer at an early stage. Three different techniques are unified in the proposed method; i) a Nonlinear filtering process to enhance the fine details within a 3x3 local neighborhood, ii) an L0 Gradient Minimization step globally preserving the high-contrast edges while suppressing low-contrast details seen as opaque fibrous tissues; and to obtain a detail mammogram after subtracting smoothed mammogram from the original mammogram, iii) Finally, an Unsharp masking technique combining detail mammogram with filtered mammogram passed through Non-linear filter, using PLIP operators which satisfy both Weber’s law and saturation characteristics of the human visual system.. An HVS-based decomposition scheme is used to analyse and visualize the malignant areas in the enhanced mammogram. The distinct arrangement of PLIP operators in the proposed framework has given rise to two methods NLUML0GMINAUTO and NLUML0GMIN, which outperforms NLUM method and other existing techniques for mammogram enhancement. The former is good at enhancing less-dense and medium-dense mammograms whereas the later enhances unseen masses within high-dense mammograms. Experimental results have stated the significance of the proposed method in effectively diagnosing breast cancer in comparison with the previous methods for mammogram enhancement. The mini-MIAS database has categorized mammograms as fatty, fatty-glandular and dense-glandular, which has helped us in focusing on enhancing images uniquely for each category. The proposed NLUML0GMIN scheme is robust and effective in identifying cancerous masses and calcifications within dense X-ray mammograms, aiding doctors in better cancer diagnosis by saving the life of countless patients with cancer.

Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Underwater imagery suffers from severe effects due to selective attenuation and scattering effects when light travels through water medium. Such damages limit the ability of vision tasks and reduce image quality. There are a lot of enhancement methods to improve the quality of underwater image. However, most of the methods produce distortion effects in the output images. The proposed natural-based underwater image color enhancement (NUCE) method consists of four steps. The first step introduces a new approach to neutralize underwater color cast. The inferior color channels are enhanced based on gain factors, which are calculated by considering the differences between the superior and inferior color channels. In the second step, the dual-intensity images fusion based on average of mean and median values is proposed to produce lower-stretched and upper-stretched histograms. The composition between these histograms improves the image contrast significantly. Next, the swarm-intelligence based mean equalization is proposed to improve the naturalness of the output image. Through the fusion of swarm intelligence algorithm, the mean values of inferior color channels are adjusted to be closed to the mean value of superior color channel. Lastly, the unsharp masking technique is applied to sharpen the overall image. Experiments on underwater images that are captured under various conditions indicate that the proposed NUCE method produces better output image quality, while significantly overcoming other state-of-the-art methods.

A Method for Microcalcifications Detection in Breast Mammograms.

Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation. Then unsharp masking and histogram modification technique has used to enhance the contrast of the image and to clarify some details like microcalcifications. Lastly, Discrete Wavelets Transform applied for detecting the abnormality. The proposed method has evaluated using the Mammographic Image Analysis Society (AS) mammography databases. The proposed method has achieved acceptable results.

A SYSTEMATIC SEARCH FOR X-RAY CAVITIES IN GALAXY CLUSTERS, GROUPS, AND ELLIPTICAL GALAXIES

ABSTRACT We perform a comprehensive study of X-ray cavities using a large sample of X-ray targets selected from the Chandra archive. The sample is selected to cover a large dynamic range including galaxy clusters, groups, and individual galaxies. Using β-modeling and unsharp masking techniques, we investigate the presence of X-ray cavities for 133 targets that have sufficient X-ray photons for analysis. We detect 148 X-ray cavities from 69 targets and measure their properties, including cavity size, angle, and distance from the center of the diffuse X-ray gas. We confirm the strong correlation between cavity size and distance from the X-ray center similar to previous studies. We find that the detection rates of X-ray cavities are similar among galaxy clusters, groups and individual galaxies, suggesting that the formation mechanism of X-ray cavities is independent of environment.

Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm

Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.

Masking-based Reinforced Edges for Blur Images

We present an unsharp masking technique which applies two parameters α and β. We assume an original image is considered to be blurred due to low pass filter such as average filter. A high pass filter is applied in that image and α and β parameter combination of low pass and high pass images concludes output image. Simulation results indicate that (α,β)=(1.0,1.2) provides the most appropriate parameter set with giving reliable performance. Experiments were completed on 18 images, and their corresponding subjective and objective performances are provided.

逆アンシャープマスキングによる画像の非写実的鮮鋭化と２値化

We present a reversed unsharp masking technique for sharpening images by producing hue halos around edges. This technique is used to binarize images. In the reversed unsharp masking, a detailed component in the usual unsharp masking is reversed and added to a base component. Images sharpened with this masking are then thresholded to generate non-photorealistic binary images.

Color to gray conversions in the context of stereo matching algorithms

This study tackles the image color to gray conversion problem. The aim was to understand the conversion qualities that can improve the accuracy of results when the grayscale conversion is applied as a pre-processing step in the context of vision algorithms, and in particular dense stereo matching. We evaluated many different state of the art color to grayscale conversion algorithms. We also propose an ad-hoc adaptation of the most theoretically promising algorithm, which we call Multi-Image Decolorize (MID). This algorithm comes from an in-depth analysis of the existing conversion solutions and consists of a multi-image extension of the algorithm by Grundland and Dodgson (The decolorize algorithm for contrast enhancing, color to grayscale conversion, Tech. Rep. UCAM-CL-TR-649, University of Cambridge, 2005) which is based on predominant component analysis. In addition, two variants of this algorithm have been proposed and analyzed: one with standard unsharp masking and another with a chromatic weighted unsharp masking technique (Nowak and Baraniuk in IEEE Trans Image Process 7(7):1068–1074, 1998) which enhances the local contrast as shown in the approach by Smith et al. (Comput Graph Forum 27(2), 2008). We tested the relative performances of this conversion with respect to many other solutions, using the StereoMatcher test suite (Scharstein and Szeliski in Int J Comput Vis 47(1–3):7–42, 2002) with a variety of different datasets and different dense stereo matching algorithms. The results show that the overall performance of the proposed MID conversion are good and the reported tests provided useful information and insights on how to design color to gray conversion to improve matching performance. We also show some interesting secondary results such as the role of standard unsharp masking vs. chromatic unsharp masking in improving correspondence matching.

An image sharpening algorithm based on fuzzy logic

This paper presents a solution to the problem of enhancing the spatial local contrast of images with nonlinear module. The proposed method that exploits the undecimated discrete fuzzy logic has much reduced noise sensitivity with respect to the linear unsharp masking technique and it permits to obtain perceptually pleasant results. The proposed method also compares favorably with other algorithms which recently have been studied to improve the behavior of the unsharp masking approach. And results are presented and discussed on different images.