Bilinear Interpolation Research Articles

Low complexity local dimming algorithm for high quality head up displays in automotive vehicles

We present a high image quality head up display (HUD) based on the local dimming liquid crystal display (LCD). The proposed HUD provides high white luminance to improve day-time visibility and low black luminance to cope with the postcard effect during the night by the local dimming technique. To reduce the computational complexity, the pixel-based boosting values for the pixel compensation are computed by simple bi-linear interpolation, complex power functions are implemented by look-up tables, some multiplications are replaced with the sums of shifted values, and the numbers of look-up tables and multipliers are reduced by sequential process. The simulation results of the proposed local dimming algorithm for 640×1280 LCD panel and 10×22 LED backlight ensure higher peak signal-to-noise ratio than 40 dB and larger power reduction performance than 73% over 7 HUD images. In addition, the postcard effect improvement is verified over images on LCD panel as well as windshield. Maximum luminance and contrast ratio on the windshield of the prototype HUD system are measured as 16,088 cd/m 2 and 342 × 10 6 :1.

Rapid Fog-Removal Strategies for Traffic Environments.

In a foggy traffic environment, the vision sensor signal of intelligent vehicles will be distorted, the outline of obstacles will become blurred, and the color information in the traffic road will be missing. To solve this problem, four ultra-fast defogging strategies in a traffic environment are proposed for the first time. Through experiments, it is found that the performance of Fast Defogging Strategy 3 is more suitable for fast defogging in a traffic environment. This strategy reduces the original foggy picture by 256 times via bilinear interpolation, and the defogging is processed via the dark channel prior algorithm. Then, the image after fog removal is processed via 4-time upsampling and Gaussian transform. Compared with the original dark channel prior algorithm, the image edge is clearer, and the color information is enhanced. The fast defogging strategy and the original dark channel prior algorithm can reduce the defogging time by 83.93-84.92%. Then, the image after fog removal is inputted into the YOLOv4, YOLOv5, YOLOv6, and YOLOv7 target detection algorithms for detection and verification. It is proven that the image after fog removal can effectively detect vehicles and pedestrians in a complex traffic environment. The experimental results show that the fast defogging strategy is suitable for fast defogging in a traffic environment.

Effective dehazing of night‐time images using open dark channel prior and wavelet transform

AbstractExisting night‐time dehazing methods had been attempting to process light and non‐light source regions based on dark channel prior (DCP). Since the bright and non‐bright regions exhibit different features, the same daytime method cannot be applied to night images because light scatter from the multiple objects non‐uniformly and DCP tends to over‐estimate the depth of the scene making the image unrealistic. To overcome this limitation, wavelet decomposition was performed so that haze remains in the low occurrence region and noise in the high occurrence region and noise was removed by soft thresholding method. In the presented approach, the open DCP (ODCP) transmission map was computed for handling light source regions and estimated transmission was refined to enhance the texture in high‐frequency part. Bilinear interpolation method of fast‐guided filtering and recursive filter in the domain transform was used for edge preservation, enhancement of texture details and smoothness. The dehazed image was constructed by correlating the coefficients of low occurrence part recovered from haze and high occurrence component. The performance analysis was compared against state‐of‐the‐art methods in terms of peak signal‐to‐noise ratio (PSNR) and Structural Similarity Index (SSIM).

Robust image hashing based on multi-view dimension reduction

In the field of multimedia security, image hashing is an effective technology for solving image content identification. Image hashing algorithm aims to map the high dimensional input features onto perceptual hashing values in terms of their perceptual content, in which visually identical images are mapped to similar digital representations. In this paper, we focus on multi-view dimensionality reduction based hashing generation, and propose a novel method, called Multi-view Dimensionality Reduction based Robust Image Hashing (MDRIH). Our MDRIH scheme maps the multi-view features into a compact binary space by considering the complementarity and similarity between image features across these different views. For preprocessing, bilinear interpolation is applied on the host image to adjust to a standard size, and ensure the different images with same hash size. Specifically, in order to improve the robustness of the algorithm against content-preserving operations, multi-view features, including structural features, edge features, and color features are extracted. After that, multi-view dimension reduction is adopted to obtain meaningful low-dimensional information from multi-feature fusion. Finally, the data in low-dimensional subspace is encoded into a compact binary sequence. The experimental results indicate that the proposed hashing method not only preserves the correlation between multiple views and the similarity among different data points within each view, but also achieves a better balance between robustness and discriminability compared to existing hashing methods.

Deep learning approach for downscaling of significant wave height data from wave models

The generation of high-resolution wave data remains a challenging task due to limitations in measurement techniques and computational resources. In the present study, we attempt to address the downscaling of ocean wave data from wave models using deep learning model, specifically convolutional neural networks. We conducted a comprehensive performance evaluation by comparing traditional methods, including bilinear interpolation, bicubic interpolation, and Kriging interpolation, against an improved Wave-Super-Resolution Convolutional Neural Network (W_SRCNN) in terms of high-resolution processing of significant wave height. Evaluation metrics including peak signal-to-noise ratio, root-mean-square error, structural similarity, cosine similarity, and no-reference image quality assessment were employed to assess the effectiveness of the methods. The results suggest that W_SRCNN outperforms other approaches in terms of overall performance, local detail preservation, and computational efficiency. Our findings demonstrate the successful application of deep learning techniques to achieve high-resolution processing of ocean data. The obtained results underscore the significance of tailored deep learning architectures to the analysis of ocean wave data, and thereby encouraging future investigations into their potential for high-fidelity reconstruction of sea surface geophysical fields from multi-source data, including ocean models, satellite observations, and buoy measurements.

Automated nailfold capillary density measurement method based on improved YOLOv5

Nailfold capillary density is an essential physiological parameter for analyzing nailfold health; however, clinical images of the nailfold are taken in many situations, and most clinicians subjectively analyze nailfold images. Therefore, based on the improved “you only look once v5” (YOLOv5) algorithm, this study proposes an automated method for measuring nailfold capillary density. The improved technique can effectively and rapidly detect distal capillaries by incorporating methods or structures such as 9mosaic, spatial pyramid pooling cross-stage partial construction, bilinear interpolation, and efficient intersection over union. First, the modified YOLOv5 algorithm was used to detect nailfold capillaries. Subsequently, the number of distal capillaries was filtered using the 90° method. Finally, the capillary density was calculated. The results showed that the Average Precision (AP)@0.5 value of the proposed approach reached 85.2 %, which was an improvement of 4.93 %, 5.24 %, and 107 % compared with the original YOLOv5, YOLOv6, and simple-faster rapid-region convolutional network (R-CNN), respectively. For different nailfold images, using the density calculated by nailfold experts as a benchmark, the calculated results of the proposed method were consistent with the manually calculated results and superior to those of the original YOLOv5.

YOLOv7-Plum: Advancing Plum Fruit Detection in Natural Environments with Deep Learning.

The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning. However, the actual plum orchard environment is complex, and the detection of plum fruits has many problems, such as leaf shading and fruit overlapping. The traditional method of manually estimating the number of fruits and the presence of pests and diseases used in the plum growing industry has disadvantages, such as low efficiency, a high cost, and low accuracy. To detect plum fruits quickly and accurately in a complex orchard environment, this paper proposes an efficient plum fruit detection model based on an improved You Only Look Once version 7(YOLOv7). First, different devices were used to capture high-resolution images of plum fruits growing under natural conditions in a plum orchard in Gulin County, Sichuan Province, and a dataset for plum fruit detection was formed after the manual screening, data enhancement, and annotation. Based on the dataset, this paper chose YOLOv7 as the base model, introduced the Convolutional Block Attention Module (CBAM) attention mechanism in YOLOv7, used Cross Stage Partial Spatial Pyramid Pooling-Fast (CSPSPPF) instead of Cross Stage Partial Spatial Pyramid Pooling(CSPSPP) in the network, and used bilinear interpolation to replace the nearest neighbor interpolation in the original network upsampling module to form the improved target detection algorithm YOLOv7-plum. The tested YOLOv7-plum model achieved an average precision (AP) value of 94.91%, which was a 2.03% improvement compared to the YOLOv7 model. In order to verify the effectiveness of the YOLOv7-plum algorithm, this paper evaluated the performance of the algorithm through ablation experiments, statistical analysis, etc. The experimental results showed that the method proposed in this study could better achieve plum fruit detection in complex backgrounds, which helped to promote the development of intelligent cultivation in the plum industry.

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.

CMIP6 models informed summer human thermal discomfort conditions in Indian regional hotspot

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Anomaly Detection in Annular Metal Turning Surfaces Based on a Priori Information and a Multi-Scale Self-Referencing Template.

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value.

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics.

In the latest research progress, deep neural networks have been revolutionized by frameworks to extract image features more accurately. In this study, we focus on an attention model that can be useful in deep neural networks and propose a simple but strong feature extraction deep network architecture, W-Net. The architecture of our W-Net network has two mutually independent path structures, and it is designed with the following advantages. (1) There are two independent effective paths in our proposed network structure, and the two paths capture more contextual information from different scales in different ways. (2) The two paths acquire different feature images, and in the upsampling approach, we use bilinear interpolation thus reducing the feature map distortion phenomenon and integrating the different images processed. (3) The feature image processing is at a bottleneck, and a hierarchical attention module is constructed at the bottleneck by reclassifying after the channel attention module and the spatial attention module, resulting in more efficient and accurate processing of feature images. During the experiment, we also tested iSAID, a massively high spatial resolution remote sensing image dataset, with further experimental data comparison to demonstrate the generality of our method for remote sensor image segmentation.

Statistical mix design method for self-compacting concrete with limestone fillers

This study presents a statistical mix design method for self-compacting concrete (SCC) with limestone fillers (LF) content that considers mortar rheological properties and material packing characteristics. The effect of LF on the rheological properties of SCC was investigated through seven SCC mixes with different LF replacement levels. A basic water demand experiment was designed to obtain the packing density of mortar, and the equivalent packing density was defined and calibrated to represent the effect of LF. The relative spread and relative flow formula were modified based on the equivalent packing density, and the bilinear interpolation method was used to analyses the mortar results.

Systematic realization of tunable-phase third-order oscillators

In this work, we introduce and verify general structures to obtain two-output tunable-phase oscillators using cascaded standard filter sections. In particular, a standard second-order high-pass, band-pass or all-pass filter followed by a first-order bi-linear filter section in a unity-gain closed loop configuration is shown to result in a third-order oscillator under certain conditions. The phase difference between the outputs of the two filter sections is a design parameter and can be tunable independent of the oscillation frequency or the start-up gain condition. Quadrature phase outputs can be obtained as special cases. Spice circuit simulations and experimental results are provided to verify the theory.

Segmentation and Angle Calculation of Rice Lodging during Harvesting by a Combine Harvester

Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios.

Study on the Tea Pest Classification Model Using a Convolutional and Embedded Iterative Region of Interest Encoding Transformer.

Tea diseases are one of the main causes of tea yield reduction, and the use of computer vision for classification and diagnosis is an effective means of tea disease management. However, the random location of lesions, high symptom similarity, and complex background make the recognition and classification of tea images difficult. Therefore, this paper proposes a tea disease IterationVIT diagnosis model that integrates a convolution and iterative transformer. The convolution consists of a superimposed bottleneck layer for extracting the local features of tea leaves. The iterative algorithm incorporates the attention mechanism and bilinear interpolation operation to obtain disease location information by continuously updating the region of interest in location information. The transformer module uses a multi-head attention mechanism for global feature extraction. A total of 3544 images of red leaf spot, algal leaf spot, bird's eye disease, gray wilt, white spot, anthracnose, brown wilt, and healthy tea leaves collected under natural light were used as samples and input into the IterationVIT model for training. The results show that when the patch size is 16, the model performed better with an IterationVIT classification accuracy of 98% and F1 measure of 96.5%, which is superior to mainstream methods such as VIT, Efficient, Shuffle, Mobile, Vgg, etc. In order to verify the robustness of the model, the original images of the test set were blurred, noise- was added and highlighted, and then the images were input into the IterationVIT model. The classification accuracy still reached over 80%. When 60% of the training set was randomly selected, the classification accuracy of the IterationVIT model test set was 8% higher than that of mainstream models, with the ability to analyze fewer samples. Model generalizability was performed using three sets of plant leaf public datasets, and the experimental results were all able to achieve comparable levels of generalizability to the data in this paper. Finally, this paper visualized and interpreted the model using the CAM method to obtain the pixel-level thermal map of tea diseases, and the results show that the established IterationVIT model can accurately capture the location of diseases, which further verifies the effectiveness of the model.

Influence of the fine aggregate particle packing effects on the paste rheological thresholds of self-compacting concrete

The paste rheological threshold theory could guide the prediction of the self-compacting concrete (SCC) performance from the paste mini-slump flow test. The prediction process was simplified, but the prediction did not consider the influence that the fine aggregate caused enough, possibly leading to a wrong mix design when the fine aggregate changed. To solve this issue, this study focused on the effect of the fine aggregate particle packing effects on the paste threshold. Based on the 3-parameter particle packing model, a fine aggregate packing parameter that can describe the fine aggregate particle packing effects more accurately was proposed to replace the original one, which considered only the particle size. Thus, the threshold calculation formulas were modified. To verify the modification, a total of five series of paste and SCC tests with different material compositions were conducted. Based on the bilinear interpolation method, the self-compacting paste zones (SCP zone) were obtained by original and modified thresholds. Through the comparison of SCP and SCC zones, the prediction accuracy of the proposed modification was evaluated by a certain evaluation method. The overlap area and relative position of the self-compacting zones were evaluated by the relative accuracy index Δε and the modification accuracy index Δ|ε’-1|, respectively. Through the modification of the paste thresholds, the overlap area of the modified SCP and SCC zones changed little. But the relative position of the modified SCP zone was closer to the SCC zone than the original SCP zone. The modified SCP zone showed more accuracy in prediction summarily. The proposed modification embodied the influence that the fine aggregate packing properties caused, expanding the description of fine aggregate particle packing effects in the paste threshold theory.

Strawberry R-CNN: Recognition and counting model of strawberry based on improved faster R-CNN

Computer technology's rapid advancement has significantly enhanced global agricultural modernization, notably improving agricultural production efficiency. Given the vulnerability of strawberries during the harvesting process, the automatic harvesting technology for strawberries necessitates a highly accurate recognition algorithm. In this paper, we introduce a model, Strawberry R-CNN, designed for intelligent recognition and counting of strawberries in natural environments. The Strawberry R-CNN model enhances strawberry recognition accuracy by refining the Faster R-CNN through several key modifications. Firstly, we replaced the VGG16 in the original Faster R-CNN with an improved multi-cascade network structure for feature extraction. This change allows for the capture of rich location data and detailed information typically absent in higher-level features. Secondly, the RoiPooling operation was replaced with RoiAlign to eliminate the error associated with the rounding method in two quantization stages. Lastly, we utilized a bilinear interpolation method for computation, preserving the floating-point number, and reducing model error. For strawberry counting, we proposed an efficient and practical evaluation method by creating an error set for strawberry counting. Experimental results demonstrated that the Strawberry R-CNN model achieved an average precision (AP) of 0.9019 for ripe strawberries and 0.8447 for immature ones, with a mean average precision (mAP) of 0.8733. The counting accuracy for ripe and immature strawberries was 99.1% and 73.7% respectively. The method presented in this work exhibits strong detection and counting capabilities, suitable for automatic monitoring, harvesting, and yield estimation of strawberries.

How does high resolution topography affect flood simulation at watershed scale: A case study in a small watershed based on tilt photography

The data source and resolution of DEM will affect the extraction of watershed features and flood simulation precision. In this paper, based on the 7 cm resolution DEM of the Liulin watershed obtained by tilt photography technology, the nearest neighbor interpolation, bilinear interpolation, and cubic convolution methods were used to resample DEMs with 1m and 30m resolution, and 6 resampled DEMs were obtained. The Aster 30m resolution DEM was downloaded from the geospatial data cloud. The watershed features were extracted from the 7 DEMs and compared. Based on these DEMs, the HEC-HMS model was established. 20 flood events were selected in the Liulin watershed for model calibration and verification. The results showed that the resampling method has little effect on the topographic features, but there were some differences between the resampling and downloaded DEM topographic features. The flood simulation results of the 1m resolution DEM-based HEC-HMS model are slightly better than that of the 30m resolution DEM-based model. The resampled 30m resolution DEM-based model performed identically with Aster 30m resolution DEM-based model. Therefore, an Aster 30m DEM can be used for flood simulation in a watershed that lacks measured high-precision topographic data.

The effects of long-term exposure to air pollution on incident mental disorders among patients with prediabetes and diabetes: Findings from a large prospective cohort

BackgroundThe association between air pollution and mental disorders has been widely documented in the general population. However, the evidence among susceptible populations, such as individuals with prediabetes or diabetes, is still insufficient. MethodsWe analyzed data from 48,515 participants with prediabetes and 24,393 participants with diabetes from the UK Biobank. Annual pollution data were collected for fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and nitrogen dioxides (NOx) during 2006–2021. The exposure to air pollution and temperature for each participant were estimated by the bilinear interpolation approach and time-weighted method based on their geocoded home addresses and time spent at each address. We employed the generalized propensity score model based on the generalized estimating equation and the time-varying covariates Cox model to assess the effects of air pollution. ResultsWe observed causal links between air pollutants and mental disorders among both prediabetic and diabetic participants, with stronger effects among those with diabetes than prediabetes. The hazard ratios were 1.18 (1.12, 1.24), 1.15 (1.10, 1.20), 1.18 (1.13, 1.23), and 1.15 (1.11, 1.19) in patients with prediabetes, and 1.21 (1.13, 1.29), 1.17 (1.11, 1.24), 1.19 (1.13, 1.25), and 1.17 (1.12, 1.23) in patients with diabetes per interquartile range elevation in PM2.5, PM10, NO2, and NOx. Furthermore, the effects were more pronounced among people who were older, alcohol drinkers, and living in urban areas. ConclusionsOur study indicates the potential causal links between long-term exposure to air pollution and incident mental disorders among those with prediabetes and diabetes. Reducing air pollution levels would significantly benefit this vulnerable population by reducing the incidence of mental disorders.

Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation.

Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.