Low-contrast Images Research Articles

SFNet-N: An Improved SFNet Algorithm for Semantic Segmentation of Low-Light Autonomous Driving Road Scenes

In recent years, considerable progress has been made in semantic segmentation of images with favorable environments. However, the environmental perception of autonomous driving under adverse weather conditions is still very challenging. In particular, the low visibility at nighttime greatly affects driving safety. In this paper, we aim to explore image segmentation in low-light scenarios, thereby expanding the application range of autonomous vehicles. The segmentation algorithms for road scenes based on deep learning are highly dependent on the volume of images with pixel-level annotations. Considering the scarcity of labeled large-scale nighttime data, we performed synthetic data collection and data style transfer using images acquired in daytime based on the autonomous driving simulation platform and generative adversarial network, respectively. In addition, we also proposed a novel nighttime segmentation framework (SFNET-N) to effectively recognize objects in dark environments, aiming at the boundary blurring caused by low semantic contrast in low-illumination images. Specifically, the framework comprises a light enhancement network which introduces semantic information for the first time and a segmentation network with strong feature extraction capability. Extensive experiments with Dark Zurich-test and Nighttime Driving-test datasets show the effectiveness of our method compared with existing state-of-the art approaches, with 56.9% and 57.4% mIoU (mean of category-wise intersection-over-union) respectively. Finally, we also performed real-vehicle verification of the proposed models in road scenes of Zhenjiang city with poor lighting. The datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pupu-chenyanyan/semantic-segmentation-on-nightime</uri> .

Long-distance mid-wave infrared super-resolution compressive imaging

Super-resolution imaging (SRI) for mid-wave infrared (MWIR) has attracted increasing attention in recent years due to the expensive price for a large number of pixels MWIR cameras. Investigation of the compressed sensing method can partially improves the resolution. However, the quality of SRI is highly related to the transfer function matrix of the optical system, which is difficult to be accurately measured because of the noise existed in MWIR detection, especially in long-distance systems. In this work, we propose a transfer function matrix calculation method which overcomes the issues of low imaging contrast and signal-to-noise ratio compared with previous studies. And we further establish a long-distance focal plane array-based compressive imaging system with a resolution of two digital micro-mirror device pixels which just satisfies the system diffraction limit. In addition, we achieve the 4 × 4 times super-resolution high-quality reconstruction of long-distance digital and physical objects with only six measurements.

Machine vision-based cutting process for LCD glass defect detection system

In this research, the automatic optical detection system is developed for detecting the sectional profile and the surface of the thin-film transistor liquid crystal display (TFT-LCD) panels after being treated through the cutting process. Traditional image processing inspection relying on pre-determined thresholding cannot achieve ideal results in slight defects in glass substrates. The proposed image pre-processing process was integrated with the deep learning technique to further enhance the detection process of inconspicuous defects in glass substrates. In addition, the photoelastic reflection lighting technique was used to highlight subtle defects in low-contrast surface images of glass substrates. When tested with the sectional profile photodetector, uniformed lighting effect is achieved by combining the concentrated light source of the inner coaxial lens with the line light source in order to test the surface coarseness-related characteristics of the glass sectional profile so as to indicate the defect while intensifying the contrast effect of the sectional profile background. When detecting the sectional profile features, it is conducted by separating the rib mark features through the U-Net network model developed for the deep learning; in result, 100% accuracy can be achieved. When detecting the sectional profile defect, Auto Encoder network model of the deep learning is used to learn the background picture retrieved from the original picture through the linear regression process. As a next step, the model is used again to predict the result by subtracting the background picture from the original picture, and then the defect position is highlighted; as a result, 98% accuracy is achieved. When detecting the model acceleration, it is conducted by revising the model weighting data format. In terms of U-Net Model, the reading time has been shortened for 4.28 s; in individual picture, the prediction time has been shortened for 0.29 s; in Auto Encoder, the model reading time has been shortened for 19.23 s; and the individual picture prediction time has been shortened for 0.94 s. As for the surface detection, the circular polariscope is developed. During the detection, the photoelastic reflection theory is employed by projecting the circularly polarized light vertically onto the panel surface in order to produce the interfering halo at the deformed area surrounding the denting defect, and the resulting features are referenced to identify the denting defect. By screening the mean luminance value of the sliding window and the discrete value, 90% detection accuracy can be achieved. In the meantime, it can also be used to pinpoint the denting defect that is over 3° in average angle change as seen on the normal line surrounding the defect.

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most preferred screening technique, generally uses low-contrast images where identification of some of the mass lesions from surrounding normal tissues is a difficult task even for skilled radiologists. Therefore, to alleviate the issue, this paper introduces an effective computer-aided detection scheme based on a novel contrast enhancement scheme for mammograms to automate the localization procedure of mass cases. Hyperbolic tangent function, a simple yet effective method, is introduced as an enhancement transfer function whose adjustable parameter is optimized by the Tunicate swarm algorithm, a nature-inspired optimization algorithm, via fitness function. To minimize the false positives generated after the detection, the median robust extended local binary pattern, a texture-based descriptor, is introduced, which possesses the ability to capture both micro- and macro-structure information from an image patch. The analysis includes four classifiers: Fisher linear discriminant analysis, K-nearest neighbor, support vector machine, and an ensemble classifier in the feature-based classification for false positive reduction (FPR). The proposed approach of automatic detection of masses when verified using two standard databases, mini-MIAS and Digital Database for Screening Mammography, comprised 68 and 550 mammograms, respectively, achieved sensitivities of 95.3% and 94.1% with 0.36 and 0.40 false positives per image after false positive reduction.

Image Processing Method Based on FGCA and Artificial Neural Network

With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.

Functionalization of Radiolabeled Antibodies to Enhance Peripheral Clearance for High Contrast Brain Imaging.

Small molecule imaging agents such as [11C]PiB, which bind to the core of insoluble amyloid-β (Aβ) fibrils, are useful tools in Alzheimer's disease (AD) research, diagnostics, and drug development. However, the [11C]PiB PET signal saturates early in the disease progression and does not detect soluble or diffuse Aβ pathology which are believed to play important roles in the disease progression. Antibodies, modified into a bispecific format to enter the brain via receptor-mediated transcytosis, could be a suitable alternative because of their diversity and high specificity for their target. However, the circulation time of these antibodies is long, resulting in an extended exposure to radiation and low imaging contrast. Here, we explore two alternative strategies to enhance imaging contrast by increasing clearance of the antibody ligand from blood. The bispecific Aβ targeting antibody RmAb158-scFv8D3 and the monospecific RmAb158 were radiolabeled and functionalized with either α-d-mannopyranosylphenyl isothiocyanate (mannose) or with trans-cyclooctene (TCO). While mannose can directly mediate antibody clearance via the liver, TCO-modified antibody clearance was induced by injection of a tetrazine-functionalized, liver-targeting clearing agent (CA). In vivo experiments in wild type and AD transgenic mice demonstrated the ability of both strategies to drastically shorten the circulation time of RmAb158, while they had limited effect on the bispecific variant RmAb158-8D3. Furthermore, single photon emission computed tomography imaging with TCO-[125I]I-RmAb158 in AD mice showed higher contrast 1 day after injection of the tetrazine-functionalized CA. In conclusion, strategies to enhance the clearance of antibody-based imaging ligands could allow imaging at earlier time points and thereby open the possibility to combine antibodies with short-lived radionuclides such as fluorine-18.

Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion.

Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model’s occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model’s Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations.

SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images

AbstractThe segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images is an essential part of the planning phase of radiation treatment to avoid the adverse effects of cancer radiotherapy treatment. Accurate segmentation is a tedious task in the head and neck region due to a large number of small and sensitive organs and the low contrast of CT images. Deep learning‐based automatic contouring algorithms can ease this task even when the organs have irregular shapes and size variations. This paper proposes a fully automatic deep learning‐based self‐supervised 3D Residual UNet architecture with CBAM(Convolution Block Attention Mechanism) for the organ segmentation in head and neck CT images. The Model Genesis structure and image context restoration techniques are used for self‐supervision, which can help the network learn image features from unlabeled data, hence solving the annotated medical data scarcity problem in deep networks. A new loss function is applied for training by integrating Focal loss, Tversky loss, and Cross‐entropy loss. The proposed model outperforms the state‐of‐the‐art methods in terms of dice similarity coefficient in segmenting the organs. Our self‐supervised model could achieve a 4% increase in the dice score of Chiasm, which is a small organ that is present only in a very few CT slices. The proposed model exhibited better accuracy for 5 out of 7 OARs than the recent state‐of‐the‐art models. The proposed model could simultaneously segment all seven organs in an average time of 0.02 s. The source code of this work is made available at https://github.com/seeniafrancis/SABOSNet.

Staining-free, Automated Sperm Analysis for In Vitro Fertilization Lab Use.

Computer-aided sperm analysis is typically used in andrology labs, not in in vitro fertilization labs, which requires staining for sperm morphology measurement. In in vitro fertilization labs, sperm analysis still relies on manual observation and suffers from subjectivity and inconsistency. We developed a system for automated measurement of sperm concentration, motility, and morphology without the need for sperm staining. The reproducibility and reliability of the system were evaluated. Thirty-five fresh semen and 25 washed samples were obtained from male partners attending for fertility investigations. Sperm concentration, motility, and morphology were automatically measured simultaneously, leveraging robust sperm tracking for concentration and motility measurement and low contrast image segmentation for morphology measurement of live sperm. Reproducibility of sperm measurements was evaluated by intraclass correlation coefficients. Reliability of sperm measurement was evaluated by Passing and Bablok regression analysis and Bland-Altman analysis. Automated measurement of concentration, motility, and morphology had intraclass correlation coefficients higher than 0.97. The regression and Bland-Altman analysis indicated that automated measurement and off-line manual benchmarking with zoomed-in images were interchangeable. Further analysis on semen and washed samples and the measurement on progressive and nonprogressive motility also showed high reproducibility and reliability. Automated sperm analysis revealed high reproducibility and reliability. The system is designed for routine use in in vitro fertilization labs to perform quantitative sperm analysis on live samples.

Industrial Scene Text Detection With Refined Feature-Attentive Network

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TongkunGuan/RFN</uri> .

Mobile bioluminescence tomography-guided system for pre-clinical radiotherapy research.

Due to low imaging contrast, a widely-used cone-beam computed tomography-guided small animal irradiator is less adept at localizing in vivo soft tissue targets. Bioluminescence tomography (BLT), which combines a model of light propagation through tissue with an optimization algorithm, can recover a spatially resolved tomographic volume for an internal bioluminescent source. We built a novel mobile BLT system for a small animal irradiator to localize soft tissue targets for radiation guidance. In this study, we elaborate its configuration and features that are indispensable for accurate image guidance. Phantom and in vivo validations show the BLT system can localize targets with accuracy within 1 mm. With the optimal choice of threshold and margin for target volume, BLT can provide a distinctive opportunity for investigators to perform conformal biology-guided irradiation to malignancy.

Infrared and Visible Image Fusion Based on Visual Saliency Map and Image Contrast Enhancement.

The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional ‘average’ fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.

Segmentation of intrinsically very low contrast magnetic resonance brain images using tensor-based DTI registration

BackgroundIn patients with hypomyelinating leukodystrophies, contrast of T1-weighted brain MRI is very low due to the lack of myelin, preventing a reliable segmentation. In diffusion tensor images the contrast is higher, thanks to anisotropy and orientation of white matter (WM) tracts. We aimed to develop and assess a tensor-guided atlas-based segmentation method suitable for segmentation of very low contrast images. Methods17 control subjects (mean age 8.0 yrs (SD 8.0)) and 27 subjects with hypomyelinating leukodystrophies (mean age 10.7 yrs (SD 10.2)) were included. DTI and 3D T1 images were segmented using a DTI-TK tensor-guided IIT-atlas-based segmentation method. For the control subjects, these segmentations were compared with a conventional segmentation of their 3D T1-weighted images. A qualitative visual assessment and a quantitative assessment using DTI metrics was performed to assess the patient segmentations. ResultsIn control subjects, the tensor-based method performed as can be expected for atlas-based segmentation methods, with Dice coefficients of 0.65, 0.72, 0.81 and 0.86 for cortical grey matter (GM), WM, deep grey matter (DGM), and thalamus, respectively. In patients with hypomyelination the visual assessment showed anatomically adequate segmentations. All tissue-specific DTI metrics differed between patients and controls. Patients with hypomyelination had reduced FA and increased mean, axial and radial diffusivities, not only in total WM, but also in the corticospinal tracts, optic radiations and thalamus. ConclusionEven in the absence of normal myelin, the presence and direction of axons allowed tensor-based registration and thereby atlas-based segmentation. We showed the applicability of the segmentation method in the context of quantitative MRI, allowing for whole-brain or regional tissue-specific and tract-specific analyses of very low contrast images.

SWTRU: Star-shaped Window Transformer Reinforced U-Net for medical image segmentation

In the last decade, deep neural networks have been widely applied to medical image segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the task of segmenting highly complex, low-contrast images of organs and tissues with high accuracy still faces great challenges. To better address this challenge, this paper proposes a novel model SWTRU (Star-shaped Window Transformer Reinforced U-Net) by combining the U-Net network which plays well in the image segmentation field, and the Transformer which possesses a powerful ability to capture global contexts. Unlike the previous methods that import the Transformer into U-Net, an improved Star-shaped Window Transformer is introduced into the decoder of the SWTRU to enhance the decision-making capability of the whole method. The SWTRU uses a redesigned multi-scale skip-connection model, which retains the inductive bias of the original FCN structure for images while obtaining fine-grained features and coarse-grained semantic information. Our method also presents the FFIM (Filtering Feature Integration Mechanism) to integration and dimensionality reduction of the fused multi-layered features, which reduces the computation. Our SWTRU yields 0.972 DICE on CHLISC for liver and tumor segmentation, 0.897 DICE on LGG for glioma segmentation, and 0.904 DICE on ISIC2018 for skin diseases’ segmentation, achieves substantial improvements over the current SoTA across 9 different medical image segment methods. SWTRU can combine feature mapping from different scales, high-level semantics, and global contextual relationships, this architecture is effective in the medical image segmentation. The experimental findings indicate that SWTRU produces superior performance on the medical image segmentation tasks.

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

Background and purposeTo investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.Materials and methodsHigh-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen’s kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists.ResultsOverall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm3) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men.ConclusionImage quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification.

Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.

Video-rate full-ring ultrasound and photoacoustic computed tomography with real-time sound speed optimization.

Full-ring dual-modal ultrasound and photoacoustic imaging provide complementary contrasts, high spatial resolution, full view angle and are more desirable in pre-clinical and clinical applications. However, two long-standing challenges exist in achieving high-quality video-rate dual-modal imaging. One is the increased data processing burden from the dense acquisition. Another one is the object-dependent speed of sound variation, which may cause blurry, splitting artifacts, and low imaging contrast. Here, we develop a video-rate full-ring ultrasound and photoacoustic computed tomography (VF-USPACT) with real-time optimization of the speed of sound. We improve the imaging speed by selective and parallel image reconstruction. We determine the optimal sound speed via co-registered ultrasound imaging. Equipped with a 256-channel ultrasound array, the dual-modal system can optimize the sound speed and reconstruct dual-modal images at 10 Hz in real-time. The optimized sound speed can effectively enhance the imaging quality under various sample sizes, types, or physiological states. In animal and human imaging, the system shows co-registered dual contrasts, high spatial resolution (140 µm), single-pulse photoacoustic imaging (< 50 µs), deep penetration (> 20 mm), full view, and adaptive sound speed correction. We believe VF-USPACT can advance many real-time biomedical imaging applications, such as vascular disease diagnosing, cancer screening, or neuroimaging.

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.