Bilateral Filtering Algorithm Research Articles

Research on Underwater Image Enhancement Algorithm

In recent years, with the continuous expansion of human activity areas, the Marine pollution caused by human activities has become increasingly serious, and the large influx of agricultural emissions and household garbage needs to rely on underwater equipment to clean up. However, to identify garbage from the complex underwater environment, it is necessary to study the underwater image enhancement algorithm. Therefore, this paper takes underwater plastic floaters as the research object, starts with the optical imaging model of underwater images, analyzes the process and mechanism of underwater optical imaging, and its impact on image quality, so as to better understand the causes of image degradation. Based on white balance algorithm, histogram equalization algorithm, bilateral filter algorithm and gamma correction method, the underwater image recognition image is improved. The principle of these methods to enhance underwater image is analyzed and applied to further improve the effect of underwater image enhancement.

Extraction of Elemental Information of Slope Steps in Open Pit Mine Based on UAV

The information of step width, height and angle of open-pit slope is of great value for open-pit mining. However, the existing methods of obtaining step element information are inefficient, and are greatly affected by staff and environmental factors, this reduces the production efficiency and acceptance accuracy of the mine. Therefore, this paper proposes a method to extract step element information based on UAV tilt photogrammetry technology. The method firstly acquires the dense point cloud data of the slope steps of the open pit based on the UAV inclined photogrammetry, preprocesses the point cloud data by using the bilateral filtering algorithm, then extracts the edge feature points of the point cloud based on the normal and curvature estimation method, and fits the step lines using the moving least squares method, and builds a step model based on the step lines, so that the profile lines at any position of the steps can be extracted on the model and obtains the step element information. The experiments show that the feature extraction method can extract the step element information efficiently and accurately, which has important application value for the production and safety of open pit mine.

Adaptive block level bilateral filtering algorithm

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.

Hardware oriented bilateral filter algorithm and architecture survey

Images and videos are often interfered with and affected by various noises. To filter out noise, researchers have proposed many filtering algorithms. bilateral filtering is a nonlinear filtering technique that can retain detailed information such as image edges well while denoising. However, it is a difficult task to implement fast and high-precision bilateral filtering algorithms in hardware platforms. In this paper, for the optimization of bilateral filtering algorithms, we analyze the gradient bilateral filtering and the piecewise approximate bilateral filtering algorithm that can be implemented in hardware, respectively. Then, we analyze and compare the implementation and optimization of bilateral filtering algorithms on FPGA and VLSI hardware architectures, and finally, make suggestions for choosing the appropriate bilateral filtering algorithms and hardware architectures for different purposes.

Research on the Real-Time Detection Method for Image Processing – Based Civil Structure Crack

Early discovery of civil structure crack facilitates timely treatment, such as repair, reinforcement and grouting. The image processing – based rapid and accurate detection method for civil structure crack brings important research value. In view of complicated construction site of civil project and complex and diversified civil structure, existing method cannot realize crack detection and geometric measurement accurately. Therefore, this article researches the real-time image processing-based detection method for civil structure crack. Bilateral filtering algorithm was used to finish smoothing processing. Crack edge of civil structure was extracted with interpolation calculation – based subpixel edge detection algorithm, to obtain more accurate crack edge position. Civil structure crack was detected based on end-to-end object detection network YOLO. The effectiveness of the model built was verified according to experimental results.

Oral CT Image Processing Based on Oral CT Image Filtering Algorithm.

The application of computer vision technology in the medical field provides more accurate technical support for oral disease detection. The research proposes the CT image denoising algorithm based on wavelet and bilateral filtering. Through the study of the imaging principle of CT image and the CT image acquisition scene, the CT image data is filtered by wavelet and bilateral filtering algorithm, and the algorithm is proposed from the peak signal-to-noise ratio, structural similarity, and the effective detection of three-dimensional image construction of the image. The test results show that the proposed algorithm has excellent performance in the aspects of peak signal-to-noise ratio, structural similarity, and error of mean square. When the proportions of Gaussian noise are 10%, 20%, 30%, 40%, and 50%, the MSE error values of the proposed algorithm are 0.002, 0.004, 0.006, and 0.007, respectively, and the performance is the best in the comparison of multiple algorithms. The contents of the research provide an important theoretical reference for the treatment of clinical oral diseases.

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.

Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography.

Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.

An Empirical Study on the Artificial Intelligence-Aided Quantitative Design of Art Images

This paper presents an indepth analysis and research on the quantitative design of fine art images through artificial intelligence algorithms. A CycleGAN-based network model for automatic generation of sketches of fine art images is constructed to extract the edge and contour features of fine art images. The network uses 512 × 1024 high-resolution art images as input and Pitchman as a discriminator. To further enhance the sketch generation effect, a bilateral filtering algorithm is added to the generator model for noise reduction, and then a K -means algorithm is used for color quantization to solve the problem of cluttered lines in the generated sketches. The experimental results show that the network model can effectively realize the automatic generation of art image sketches and can retain the detailed part of the costume information well. A rendering platform is built to realize the application of art image generation algorithms and coloring algorithms. The platform integrates the functions of image preprocessing, sketch generation, and sketch coloring, demonstrates the results of the main research content of this paper, and finally increases the interest of the system through the rendering function of the art image grid, which further improves the practicality of the platform.

Noise Removal Algorithm for Out-of-focus Blurred Images Based on Bilateral Filtering

The feature resolution of traditional methods for fuzzy image denoising is low, for the sake of improve the strepitus removal and investigation ability of defocused blurred night images, a strepitus removal algorithm based on bilateral filtering is suggested. The method include the following steps of: Building an out-of-focus blurred night scene image acquisition model with grid block feature matching of the out-of-focus blurred night scene image; Carrying out information enhancement processing of the out-of-focus blurred night scene image by adopting a high-resolution image detail feature enhancement technology; Collecting edge contour feature quantity of the out-of-focus blurred night scene image; Carrying out grid block feature matching design of the out-of-focus blurred night scene image by adopting a bilateral filtering information reconstruction technology; And building the gray-level histogram information location model of the out-of-focus blurred night scene image. Fuzzy pixel information fusion investigation method is used to collect gray features of defocused blurred night images. According to the feature collection results, bilateral filtering algorithm is used to automatically optimize the strepitus removal of defocused blurred night images. The simulation results show that the out-of-focus blurred night scene image using this method for machine learning has better strepitus removal performance, shorter time cost and higher export peak signal-to-strepitus proportion.

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

It aimed to explore the application value of high-frequency ultrasound image based on the wavelet bilateral filtering algorithm in the repair evaluation of hypertrophic scar and keloid, as well as the expression change of nerve growth factor (NGF). 72 patients with scars admitted to hospital from October 2018 to October 2019 were chosen and classified as hypertrophic scar patients (group A) and keloid patients (group B), with 36 cases in each group. All patients received scar repair treatment, and another 30 normal skin volunteers (group C) were selected. High-frequency ultrasound images based on the wavelet bilateral filter algorithm were utilized for skin examination. The expression differences of NGF and pro-NGF in each group were measured by immunohistochemical staining and Western blot. The results showed that the denoising effect (signal-to-noise ratio (PSNR) = 33.2762), structural similarity (SSIM = 0.8963), and edge similarity (FoM = 0.2975) of the proposed algorithm were better than those of bilateral filtering and wavelet soft-threshold algorithms. The skin thickness of groups A and B was considerably higher relative to that of group C ( P < 0.05), but the echo intensity of the skin dermis of groups A and B was evidently inferior to that of group C ( P < 0.05). The results of immunohistochemistry showed that in contrast to group C, the average optical density of NGF in groups A and B increased remarkably ( P < 0.01). In contrast to group C, the positive expression of pro-NGF in groups A and B was notably reduced ( P < 0.05). Western blot results showed that relative to group C, the expression of NGF protein in groups A and B was increased greatly ( P < 0.01), but the expression of pro-NGF protein in groups A and B was reduced ( P < 0.01). The results suggested that the wavelet bilateral filter algorithm can be adopted to evaluate pathological skin scars, and pathological skin scar formation was closely related to the expression levels of NGF and pro-NGF.

Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion

Background and ObjectiveBreast cancer is a fatal threat to the health of women. Ultrasonography is a common method for the detection of breast cancer. Computer-aided diagnosis of breast ultrasound images can help doctors in diagnosing benign and malignant lesions. In this paper, by combining image decomposition and fusion techniques with adaptive spatial feature fusion technology, a reliable classification method for breast ultrasound images of tumors is proposed. MethodsFirst, fuzzy enhancement and bilateral filtering algorithms are used to process the original breast ultrasound image. Then, various decomposition images representing the clinical characteristics of breast tumors are obtained using the original and mask images. By considering the diversity of the benign and malignant characteristic information represented by each decomposition image, the decomposition images are fused through the RGB channel, and three types of fusion images are generated. Then, from a series of candidate deep learning models, transfer learning is used to select the best model as the base model to extract deep learning features. Finally, while training the classification network, adaptive spatial feature fusion technology is used to train the weight network to complete deep learning feature fusion and classification. ResultsIn this study, 1328 breast ultrasound images were collected for training and testing. The experimental results show that the values of accuracy, precision, specificity, sensitivity/recall, F1 score, and area under the curve of the proposed method were 0.9548, 0.9811, 0.9833, 0.9392, 0.9571, and 0.9883, respectively. ConclusionOur research can automate breast cancer detection and has strong clinical utility. When compared to previous methods, our proposed method is expected to be more effective while assisting doctors in diagnosing breast ultrasound images.

A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images

In the recent past, biomedical domain has become popular due to digital image processing of accurate and efficient diagnosis of clinical patients using Computer-Aided Diagnosis (CAD). Appropriate and punctual disease identification and treatment arrangement directs to enhance superiority of life and improved life hope in Alzheimer Disease (AD) patients. The cutting-edge approaches that believe multimodal analysis have been shown to be efficient and accurate are improved compared with manual analysis. Many tools have been introduced for detection of Alzheimer but still it is a financially high costly diagnosis system gives detection of disease with low accuracy and efficient due to performance of Magnetic Resonance Imaging (MRI) scanning devices. A novel methodology is proposed in this research as CAD process using various algorithms for predicting AD. The MRI images from scanning device are a highly noisy image due to thermal activities of hardware involved in scanning device. The image restoration technique is applied using 2D Adaptive Bilateral Filter (2D-ABF) algorithm. The quality of image in terms of brightness and contrast are improved using image enhancement techniques based on Adaptive Histogram Adjustment (AHA) algorithm. The Region of Interest of Alzheimer disease is segmented using Adaptive Mean Shift Modified Expectation Maximization (AMS-MEM) algorithm. The various features are calculated using second order 2-Dimensional Gray Level Co-Occurrence Matrix (2D-GLCM). Based on selection of features, the Deep Learning (DL) approach is used to classify the disease images and its stages. The Deep Convolutional Neural Network (DCNN) is the classification technique implemented to classify disease for proper diagnostic decision making. The experimental results prove that the proposed methodology provides better accuracy and efficiency than existing system.

The Retinex based improved underwater image enhancement

The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.

License Plate Recognition

<p>License Plate Recognition (LPR) system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image and by extracting their number plate from whole vehicle image, it displays the number plate information into text. The overall accuracy and efficiency of whole LPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The approach used to segment the image is bilateral filtering algorithm and canny edge detection algorithm. Then we predict the license plate from processed image using py–tesseract OCR and match the retrieved text which is vehicle number plate with database. Finally we get the details of the particular vehicle from the database.</p>

水下二维及三维距离选通成像去噪技术研究

Compared with traditional underwater cameras, the detection distance of underwater range-gated imaging can be increased by 2-3 times. Furthermore, based on this technology, fast high-resolution 3D imaging can be achieved. It has great potentials in underwater target detection and recognition, automatic navigation, marine scientific research and natural resources exploration. Although range-gated imaging can suppress the backscattering noise by space slicing, and achieve higher quality images, the backscattering noise of the sampling water volume still exists in gated images, resulting in low image signal-to-noise ratio and contrast, especially for distant targets or low reflectance targets. This paper systematically introduced author's research of deblurring methods for 2D and 3D range-gated imaging. In 2D imaging, two methods were proposed:the first was an algorithm of self-adaptive double-plateau histogram equalization to improve image contrast and better meet human vision; the second was to use a water-noise-reference denoising algorithm to improve the signal-to-noise ratio of target gated images. In 3D imaging, there were also two methods proposed:one was to use the water-noise-reference denoising algorithm for 3D reconstruction, and the other was to use the threshold-dependent joint bilateral filter algorithm for enhancing 3D images. The four proposed methods can be used independently or in combination for denoising enhancement of underwater range gated imaging.

改进型自适应双边滤波算法

改进型自适应双边滤波算法

Static gesture segmentation technique based on improved Sobel operator

The gesture recognition technology based on computer vision can provide a more natural way of human–computer interaction, and become a research hotspot in the field of gesture recognition. This study proposes an improved Sobel operator which is used for static hand contour extraction. The method is based on the noise existing in the picture, and a bilateral filtering algorithm is employed to denoise and preserve the edges to a large extent. Based on the colour space, the elliptical skin colour model is used to extract the skin colour domain, and the hand is separated according to the block domain feature. Finally, in order to solve the oblique indirection problem of the traditional Sobel operator, two oblique direction templates are added and the weights are reassigned. Then the improved Sobel operator is used to extract the gesture edges, and compared with the classical extraction edge operator Laplace and Canny extraction effects. The experimental results show that the proposed algorithm has high efficiency and accuracy in static gesture segmentation in a simple environment.

Development of Algorithm for Optimally Weighted Non-Iterative Bilateral Filter (OW-NI-BF) to Eliminate Gaussian Noise

Gaussian filter is linear that smooth independent of edges and details of the image. The standard bilateral filter is effective for low-density noise. However, the performance of filter degrades with an increase in noise level. We designed a non-iterative bilateral filter algorithm which is robust for large noise levels, but lacks low noise levels the performance. To get the best denoising performance out of these two, standard bilateral filter and proposed non-iterative bilateral filter is combined in weighted fashion using Stein's unbiased risk estimate. Thus, the proposed optimally weighted non-iterative bilateral filter (OW-NI-BF) algorithm is guaranteed to perform better than either of the component filter. It is non-linear, local and non-iterative that works on geometric closeness and gray level similarities irrespective of smoothing edge. In Gaussian noise scenarios, the performance of the proposed OW-NI-BF algorithm is compared with various methods. Quantitative and visual denoising results demonstrate significant improvement over the original filter. PSNR and IQI are used to measure the quality of the de-noised image.

Trilateral Smooth Filtering for Hyperspectral Image Feature Extraction

Traditional bilateral filtering (BF) cannot extract hyperspectral image (HSI) features well when the center pixel of the neighborhood pixel set is a noise point in the process of filtering the HSI. In this letter, a trilateral smooth filtering (TRSF) is presented. The proposed algorithm avoids the above-mentioned limitation problem in the BF algorithm. TRSF is successfully applied to the feature extraction of three actual HSIs. To prove the effectiveness of the proposed algorithm, support vector machines are used to classify the extracted features. Experimental results show that the proposed feature extraction method is simple and effective.