Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering

In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.

A fast,edge-preserving,distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.

An Image Denoising Method Based on Multiscale Wavelet Thresholding and Bilateral Filtering

A novel image denoising method is proposed based on multiscale wavelet thresholding (WT) and bilateral filtering (BF). First, the image is decomposed into multiscale subbands by wavelet transform. Then, from the top scale to the bottom scale, we apply BF to the approximation subbands and WT to the detail subbands. The filtered subbands are reconstructed back to approximation subbands of the lower scale. Finally, subbands are reconstructed in all the scales, and in this way the denoised image is formed. Different from conventional methods such as WT and BF, it can smooth the low-frequency noise efficiently. Experiment results on the image Lena and Rice show that the peak sig-nal-to-noise ratio (PSNR) is improved by at least 3 dB and 0.7 dB compared with using the WT and BF, respectively. In addition, the computational time of the proposed method is almost comparable with that of WT but much less than that of BF.

Bilateral Filter for the Optimization of Composite Structures

In the present study,we propose to integrate the bilateral filter into the Shepard-interpolation-based method for the optimization of composite structures.The bilateral filter is used to avoid defects in the structure that may arise due to the gap/overlap of adjacent fiber tows or excessive curvature of fiber tows.According to the bilateral filter,sensitivities at design points in the filter area are smoothed by both domain filtering and range filtering.Then,the filtered sensitivities are used to update the design variables.Through several numerical examples,the effectiveness of the method was verified.

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

In existing methods for segmented images,either edge point extraction or preservation of edges,compromising contrast images is so sensitive to noise.The Degeneration Threshold Image Detection(DTID)framework has been proposed to improve the contrast of edge filtered images.Initially,DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images.This filter decomposes input images into base layers in the DTID framework.With minimal filtering time,Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation.In the DTID framework,Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise.This Shift-Invariant Filtering estimates value across edges for removing outliers(i.e.,noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter(SIDF).At last,Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image.It normalizes the translation and rotation of images.With this,Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy,rate,and filtering time on contrast images.Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54%and improves average contrast enhancement quality by 27%compared to GUMA,HMRF,SWT,and EHS.It provides better performance on the enhancement of average contrast enhancement quality by 28%,detection accuracy rate by 26%,and reduction in filtering time taken on contrast images by 30%compared to state-of-art methods.

Detection of fabric defects based on bilateral filter and frangi filter

Aimed at low contrast effect on fabric detection,a method based on bilateral filter and frangi filter is proposed. Firstly,in order to reduce the influence of fabric background texture information on the detection results,bilateral filter is used to deal with the fabric image. Then frangi filter is used to filter the fabric image after bilateral filtering to enhance the fabric defect area information. Finally,a maximum entropy method is implemented on the fabric image after frangi filtering to separate the defected area. Experimental results show that the proposed method can effectively detect defects.

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases.Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure.Although various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s edges.The curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge enhancement.The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges.Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges.Therefore,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image.Afterwards C mean thresholding is used for the extraction of vessel.The recommended fusion approach is assessed on DRIVE dataset.Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result.The results demonstrate that the recommended method outperforms the traditional approaches.

Edge preserving super-resolution algorithm using multi-stage cascaded joint bilateral filter

Super-resolution(SR)algorithms address the inabilities of poor imaging devices,there by producing high quality images with enhanced resolution.We propose a new SR approach which produces sharp high resolution(HR)image using its low resolution(LR)counterparts.The proposed method uses geometric duality for spatially adapting covariance-based interpolation(CBI).To preserve edge information,a multi-stage cascaded joint bilateral filter(MSCJBF)is proposed as an intermediary stage.These edges are incorporated in the high frequency subbands obtained by the stationary wavelet transform(SWT),through nearest neighbor interpolation(NNI)method.Prior to the NNI process,the high frequency subbands undergo two-lobed lanczos interpolation to achieve the desired resolution enhancement.The quantitative and qualitative analysis for various test images prove the superiority of our method.

A Nowcasting Technique Based on Application of the Particle Filter Blending Algorithm

To improve the accuracy of nowcasting, a new extrapolation technique called particle filter blending was configured in this study and applied to experimental nowcasting. Radar echo extrapolation was performed by using the radar mosaic at an altitude of 2.5 km obtained from the radar images of 12 S-band radars in Guangdong Province,China. The first bilateral filter was applied in the quality control of the radar data; an optical flow method based on the Lucas–Kanade algorithm and the Harris corner detection algorithm were used to track radar echoes and retrieve the echo motion vectors; then, the motion vectors were blended with the particle filter blending algorithm to estimate the optimal motion vector of the true echo motions; finally, semi-Lagrangian extrapolation was used for radar echo extrapolation based on the obtained motion vector field. A comparative study of the extrapolated forecasts of four precipitation events in 2016 in Guangdong was conducted. The results indicate that the particle filter blending algorithm could realistically reproduce the spatial pattern, echo intensity, and echo location at 30-and 60-min forecast lead times. The forecasts agreed well with observations, and the results were of operational significance. Quantitative evaluation of the forecasts indicates that the particle filter blending algorithm performed better than the cross-correlation method and the optical flow method. Therefore, the particle filter blending method is proved to be superior to the traditional forecasting methods and it can be used to enhance the ability of nowcasting in operational weather forecasts.

Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.