Unsharp Masking Research Articles

Enhanced deep learning models for automatic fish species identification in underwater imagery

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R–FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R–FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

Multi-headed U-Net: an automated nuclei segmentation technique using Tikhonov filter-based unsharp masking

ABSTRACT An automated nuclei segmentation is the key technique for understanding and analyzing cellular properties, which are helpful for disease diagnosis and support computer-aided digital pathology. However, this task is challenging because of the variability in nuclei size and morphology, which results in blurry boundaries and overlapping nuclei. To address such issues, a multi-headed U-Net convolutional neural network (CNN) architecture has been proposed. This architecture has multiple heads to extract multi-resolution features of the source image by using different kernel sizes of the filters. The source images are pre-processed using an unsharp masking approach based on the Tikhonov filter. The Tikhonov filter decomposes the input image into low-frequency and high-frequency band images. The unsharp masking method improves the high-frequency information of the input image by primarily enhancing features such as boundaries, contours, and fine details. We have incorporated intersection over union (IOU) and F1Score as measures along with accuracy for our proposed objective functions. The proposed objective functions are tried to be maximized by the optimization algorithm, and the higher value of the metrics indicates better segmentation performance in the spatial domain during the testing phase. The proposed method attained IOU(JI), Accuracy, Precision, and F1Score values as 0.8299, 0.9642, 0.8918, and 0.9070, respectively. The quantitative and qualitative experimental outcomes indicate that our proposed technique outperforms the state-of-the-art techniques.

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.

Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy.

Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis.

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

A Triadic Approach for Enhancement of Underwater Images Using Adaptive Colour Correction with Unsharp Masking and CLAHE Implementation

A Triadic Approach for Enhancement of Underwater Images Using Adaptive Colour Correction with Unsharp Masking and CLAHE Implementation

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Detection of Illegal Goods using X-ray Image Enhancement Algorithm

Abstract: In order to facilitate the security personnel to identify the items in the X-ray image, it is necessary to enhance the security X-ray image to highlight the color, edge, shape and other details of the different items in the image, so that the security personnel can identify the inspected items more accurately and quickly, to ensure the safe operation of the airport. Aiming at the problem of color distortion in CLAHE enhanced airport security X-ray images, an X-ray image enhancement algorithm combining USM+CLAHE is proposed. In this work, First, we calculate the grayscale images on the R, G, and B channels of the X-ray image and perform Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement respectively, and then merge the enhanced R, G, and B grayscale images. Next, we perform the Unsharp Mask (USM) sharpening operation on the Xray image enhanced by CLAHE, and finally merge the original image and the USM sharpened image according to the weight. The experimental results show that the USM+CLAHE algorithm can effectively enhance the security X-ray image, and at the same time can suppress the color distortion of the enhanced image.

Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking

Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking

Unsharp Masking of Medical Images using Rolling Guidance Filter

Medical Images consists of both high frequency and low frequency information needed by Physician for diagnostics and treatment purpose. Since, human eye is sensitive to high frequency information, the enhancement of high frequency information of medical image is very important. Most image enhancement techniques use the traditional unsharp mask filter to enhance the detail and edge information of medical image. While enhancing the detail and edge information, Gaussian Filter in traditional Unsharp Masking Filter introduces halo effects around edges. To remove this halo effects in enhanced images, this paper proposes a novel image enhancement filter in which Gaussian Filter is replaced by rolling guidance filter. This method provides satisfying results over Traditional Unsharp Mask Filter, Amended Unsharp Mask Filter, Anisotropic Diffusion based Unsharp Mask Filter and Guided Filtering based Unsharp Mask Filter in terms of high frequency information measures like mean gradient, spatial frequency and sharpness.

REAL-TIME IMPLEMENTATION OF LWT BASED NOVEL IR AND VI FUSION ALGORITHM USING RASPBERRY PI PLATFORM

One of the most important and active areas of image processing research is visible and thermallight image fusion. Moreover, real-time visible and thermal-light image fusion has been utilized in multiple kinds of applications such as surveillance devices, military applications, medical diagnosis, remote sensing, etc. Hence, this paper suggests a real-time application of visible and infrared (V-I) image fusion using lift wavelet transform (LWT) and implemented on the Raspberry Pi. The proposed morphological operations-based unsharp masking enhancement technique overcomes the low contrast issue of the fused image. Next, the LWT provides the good feature (smooth and detail) layers of visible image (VI) and infrared image (IRI) when compared to other transformations. Due to this, we have overcome the problem of spatial distortions in the fused image. Moreover, the proposed novel fusion techniques such as, mean-weighted fusion rule based on filters and max-weighted fusion rule based on filters generate the fused image with improved texture information and overcome the problem of edge information loss. The real-time application of the V-I image fusion is implemented through the portable hardware device Raspberry Pi. Finally, the experimental results show that the suggested framework has produced a fused image with improved visual quality, rich edge information, high contrast, low spatial distortions, and more texture information, when compared to the results of existing methodologies.

A Polarization-Free Imaging Technique Based on Unsharp Masking Model

A Polarization-Free Imaging Technique Based on Unsharp Masking Model

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>

An Improved Inception Network to classify black tea appearance quality

To conveniently and precisely evaluate orthodox black tea appearance quality, we here present a novel method based on computer vision integrated image processing and deep learning. Tea images were collected using the custom-built image acquisition device and processed with Adaptive Local Tone Mapping (ALTM), and Unsharp Masking to optimize illumination and sharpness. These images were then used to train six convolutional neural networks (CNNs) to find suitable network structure by transfer learning. It was found that the CNNs constructed with the MBconv modules had better performance in this task. Consequently, a CNN classification model (Improved Inception Network) based on the MBconv modules was constructed and trained, which yielded a test accuracy of 95%, performed better than the other CNNs; the test accuracy of original Inception V3 was 89%. The proposed method obtained 97.22% accuracy for independent set in validation, which demonstrated the viability of applying image processing and deep learning approaches to solve practical problems in the field of tea assessment.

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.

Auto-metric graph neural network for paddy leaf disease classification

In traditional methods, rice plant diseases can be identified individually by experts, but laboratory testing takes a lot of time. As a result, it reduces agricultural productivity and causes economic losses for farmers. To address these, rapid and effective systems for identifying and classifying rice plant diseases need to be developed. This article proposes an Auto-metric Graph Neural Network (AGNN) for the classification of paddy leaf disease. Initially, images are gathered via the dataset of Rice Leaf Disease Image Samples. The collected images are preprocessing by utilizing Anisotropic Diffusion Filter-Based Unsharp Masking with crispening. These preprocessed paddy leaf images are supplied to Bayesian fuzzy clustering for the purpose of segmentation. The segmented pictures are fed to AGNN for categorizing paddy leaf infection image as Bacterial Blight, Blast, Brown Spot and Tungro. The paddy leaf disorder classification-AGNN technique attains accuracies of 8.65%, 10.76%, 18.34% and 13.19% compared to the existing methods.

Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline

BackgroundChest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usually fails when clinically applied to various machines. To overcome this problem, we propose an image manipulation pipeline.MethodsA total of 15,010 chest x-rays from systems with different generators/detectors were retrospectively collected from five institutions from May 2020 to February 2021. We developed an AI model to classify pulmonary abnormalities using x-rays from a single system. Then, we externally tested its performance on chest x-rays from various machine specifications. We compared the area under the receiver operating characteristics curve (AUC) of AI models developed using conventional image processing pipelines (histogram equalization [HE], contrast-limited histogram equalization [CLAHE], and unsharp masking [UM] with common data augmentations) with that of the proposed manipulation pipeline (XM-pipeline).ResultsThe XM-pipeline model showed the highest performance for all the datasets of different machine specifications, such as chest x-rays acquired from a computed radiography system (n = 356, AUC 0.944 for XM-pipeline versus 0.917 for HE, 0.705 for CLAHE, 0.544 for UM, ple 0.001, for all) and from a mobile x-ray generator (n = 204, AUC 0.949 for XM-pipeline versus 0.933 for HE, p = 0.042, 0.932 for CLAHE (p = 0.009), 0.925 for UM (p = 0.001).ConclusionsApplying the XM-pipeline to AI training increased the diagnostic performance of the AI model on the chest x-rays of different machine configurations.Relevance statementThe proposed training pipeline would successfully promote a wide application of the AI model for abnormality screening when chest x-rays are acquired using various x-ray machines.Key points• AI models developed using x-rays of a specific machine suffer from generalization.• We proposed a new image processing pipeline to address the generalization problem.• AI models were tested using multicenter external x-ray datasets of various machines.• AI with our pipeline achieved the highest diagnostic performance than conventional methods.Graphical

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).

Binocular Vision-Based Non-Singular Fast Terminal Control for the UVMS Small Target Grasp

Autonomous underwater manipulation is very important for the robotic and intelligence operations of oceanic engineering. However, a small target often involves limited features and results in inaccurate visual matching. In order to improve visual measurement accuracy, this paper has proposed an improved unsharp masking algorithm to further enhance the weak texture region of blurred and low contrast images. Moreover, an improved ORB feature-matching method with adaptive threshold, non-maximum suppression and improved random sample consensus has also been proposed. To overcome unknown underwater disturbances and uncertain system parameters in the underwater robotic manipulations, an adaptive non-singular terminal sliding mode controller has been proposed with a quasi-barrier function to suppress the chattering problem and improve grasp accuracy for small target. Oceanic experiments have been conducted to prove the performance of the proposed method.

Approximated unsharp masking on equi-hue plane in RGB color space

Approximated unsharp masking on equi-hue plane in RGB color space