Fast Bi-dimensional empirical mode decomposition(BEMD) based on variable neighborhood window method

The data interpolation is an essential part of Bidimensional Empirical Mode Decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However, only one or two of the RBF interpolators have been utilized for BEMD so far. This paper employs many RBF interpolators for BEMD, compares their performances, and finds out the useful ones for BEMD especially in the image filtering application. We propose to apply the BEMD approach with the adequate interpolation function in the image denoising domain. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. The analysis is done using real images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters of the BEMD process. The study is believed to work as a guideline in the area of BEMD based real image in the filtering application.

In this work, a hierarchical algorithm and a hardware architecture for bi-dimensional fast empirical mode decomposition (B-EMD) is proposed. Using the proposed low cost architecture, decomposing images, video and hyper spectral images using the power of dedicated hardware will be possible.

The performance of interpolation algorithm used in bi-dimensional empirical mode decomposition directly affects its popularization and application. Therefore, the research on interpolation algorithm is more reasonable, accurate and fast. So far, in the interpolation algorithm adopted by the bi-dimensional empirical mode decomposition, an adaptive interpolation algorithm can be proposed according to the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. The parameters involved in the interpolation process are optimized by particle swarm optimization algorithm, and the optimal parameters are obtained, which can solve the problem of low efficiency and low precision of interpolation algorithm used in bi-dimensional empirical mode decomposition. It solves the problem that the image cannot be decomposed to obtain accurate and reliable bi-dimensional intrinsic modal function, and realize the fast decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

This paper presents a new approach of edge detection utilizing bidimensional empirical mode decomposition (BEMD) technique. For this purpose a recently developed fast and adaptive BEMD (FABEMD) is employed to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of the surface interpolation, which enables fast decomposition and well characterized BIMFs. Since the first BIMF provides the highest local spatial variations and/or scales of the image, this BIMF is then processed for obtaining the edge. Binarization and morphological operations are applied as post processing operations to the first BIMF to achieve the desired edge map. The proposed method is compared with two other standard techniques namely, Canny and Sobel edge operators. Simulation results with real images demonstrate the efficacy of the proposed algorithm for edge detection.

The pansharpening is a combination of multispectral (MS) and panchromatic (PAN) images that produce a high-spatial-spectral-resolution MS images. In multiresolution analysis–based pansharpening schemes, some spatial and spectral distortions are found. It can be reduced by adding spatial detail images of the PAN image into MS images. In the convolution neural network– (CNN) based method, the lowpass filter image extracted by the CNN model when MS and PAN images are directly applied into the input. The feature values are very high and reduce the conversion efficiency. In the proposed scheme, bi-dimensional empirical mode decomposition is used to extract the spatial detail information of the PAN image to reduce the feature values of the input. This extracted PAN image information is applied to the CNN to produce the non-linear changes in the image pixels and transformed into the perfect spatial detail image. It identifies the spatial and spectral detail quantity for the proposed scheme and it also varies with the different datasets automatically of the same satellite images. Simulation results in the context of qualitative and quantitative analysis demonstrate the effectiveness of proposed scheme applied on datasets collected by different satellites.

Bidimensional empirical mode decomposition (BEMD) for extraction of gravity anomalies associated with gold mineralization in the Tongshi gold field, Western Shandong Uplifted Block, Eastern China

Slope stability is a key problem for safe submarine channel operation on muddy coasts. The early detection and identification of slope instabilities is important to allow safety measures to be taken in a timely manner. Owing to the difficulties in predicting or providing early forecasts of slope collapse in muddy submarine channels, this study introduces a bidimensional empirical mode decomposition (BEMD) with adaptive time–frequency resolution to identify slope instabilities using sound prints of sub-bottom profile images. The results show that three bidimensional intrinsic mode function (BIMF) images decomposed by BEMD with frequencies ranging from high to low can depict the spatial variation in the sound prints. The second and third BIMF images reveal the spatial relationship between the sound prints and the slope abnormalities at the early stage of slope collapse. This approach effectively identifies the early sound prints and the spatial information of the sub-bottom profile images during the process of slope collapse, which can improve abnormal feature recognition. This method will help in the prediction and early forecast of channel slope instability and collapse.

With varying illumination conditions, facial features obtained from images are distorted nonlinearly by variant lighting intensity and direction, so face recognition becomes very difficult. According to the "common assumption", illumination varies slowly and the face intrinsic feature (including 3D surface and reflectance) varies rapidly in local area, we can then consider high frequency features that represent the face intrinsic structure. FABEMD8 (Fast and Adaptive Bidimensional Empirical Mode Decomposition) is a fast and adaptive method of BEMD22 (Bidimensional Empirical Mode Decomposition), and not using time-consuming plane interpolation computation, it can decompose the image into multilayer high frequency images representing detail features and low frequency images representing analogy features. But we cannot make a quantitative analysis of how many detail features can be used to eliminate illumination variation. So we propose two measures to quantify the detail features, and with these measure weights, we can activitate FABEMD based multilayer detail images matching for face recognition under varying illumination. With PCA, the experiments based on Yale face database B and MU PIE face database show that the method proposed in this paper can get remarkable performance.

Objective: The three steps of brain image processing – preprocessing, segmentation, and classification are becoming increasingly important in patient care. The aim of this article is to present a proposed method in the mentioned three-steps, with emphasis on the preprocessing step, which includes noise removal and contrast enhancement. Methods:The fast and adaptive bidimensional empirical mode decomposition and the anisotropic diffusion equation as well as the modified combination of top-hat and bottom-hat transforms are used for noise reduction and contrast enhancement. Fast C-means clustering with enhanced image is used to detect tumors and the tumor cluster corresponds to the maximum centroid. Finally, Ensemble learning is used for classification. Result:The Figshare brain tumor dataset contains magnetic resonance images used for data selection. The optimal parameters for both noise reduction and contrast enhancement are investigated using a tumor contaminated with Gaussian noise. The results are evaluated against state-of-the-art results and qualitative performance metrics to demonstrate the dominance of the proposed approach. The fast C-means algorithm is applied to detect tumors using twelve enhanced images. The detected tumors were compared to the ground truth and showed an accuracy and specificity of 99% each, and a sensitivity and precision of 90% each. Six statistical features are retrieved from 150 enhanced images using wavelet packet coefficients at level 4 of the Daubechies 4 wavelet function. These features are used to develop the classifier model using ensemble learning to create a model with training and testing accuracy of 96.7% and 76.7%, respectively. When this model is applied to classify twelve detected tumor images, the accuracy is 75%; there are three misclassified images, all of which belong to the pituitary disease group. Conclusion: Based on the research, it appears that the proposed approach could lead to the development of computer-aided diagnosis (CADx) software that physicians can use as a reference for the treatment of rain tumor.

X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. Some methods have been applied in XPCI field, but wavelet-based decomposition approaches often result in loss of original data. To explore the application value of a novel image fusion method for XPCI system and computed tomography (CT) system. The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. The effectiveness of other image fusion methods has been compared, such as discrete wavelet transforms and anisotropic diffusion fusion. The δ-guided FABEMD-MGPCNN method achieved either the first or second position in objective evaluation metrics with biological samples, as compared to other image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT show expected results to retain the feature information. The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.

Recently, research endeavors have shown the potentiality of Cycle-Consistent Adversarial Networks (CycleGAN) in style transfer. In Cycle-Consistent Adversarial Networks, the consistency loss is introduced to measure the difference between the original images and the reconstructed in both directions, forward and backward. In this work, the combination of Cycle-Consistent Adversarial Networks with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to perform style transfer on images. In the proposed approach the cycle-consistency loss is modified to include the differences between the extracted Intrinsic Mode Functions (BIMFs) images. Instead of an estimation of pixel-to-pixel difference between the produced and input images, the FABEMD is applied and the extracted BIMFs are involved in the computation of the total cycle loss. This method enriches the computation of the total loss in a content-to-content and style-to-style comparison by connecting the spatial information to the frequency components. The experimental results reveal that the proposed method is efficient and produces qualitative results comparable to state-of-the-art methods.

Active remotely sensed data can be used to perform a variety of forestry tasks including stand characterization, inventory, and management of forest and fire behavior modeling. The present work investigates the potential of Airborne Laser Scanning (ALS) derived methods applied in the deciduous forest by processing an individual tree detection (ITD) based on canopy Height model (CHM) and tree segmentation of larger-area point clouds. Different algorithms are tested and their performances are evaluated to show which of them can provide the most adequate number of trees compared with the ground truth. Tree scale information is used in order to determine stand age. The forest height, structure, and density are specified by applying individual tree Detection (ITD) to calculate some forest attributes such as stem volume, forest uniformity, and biomass estimation. The major aim of this post is to examine the state of the forest to monitor it in real-time. We assume that utilizing the LM algorithm, which was originally built for ITD from LiDAR data, trees should be automatically distinguished from the ALS-derived CHM with reasonable accuracy. As a result, the present research work studies the fixed treetop window size (FWS), fixed smoothing window size (SWS), and variable window (VW) effect on ITD performance (RMSE=3.4% and R=0.88). It is obvious, from the obtained results that smaller window sizes result in more trees. In fact, the smallest trees obscured by the largest trees containing the highest points in the neighborhood are often ignored by large windows. Crown delineation is also explored to extract the height of the trees, radius crown and, 3D coordinates and to compare them to those detected by a Low Bluetooth sensor “iBeacon.”.

The multi-focus image fusion with adaptable windows (MF-AW) algorithm for multiple images improves the results of the linear combination of images with variable windows (CLI-VV—from its Spanish acronym) algorithm, using a unique decision map and applying parallel programming. Other algorithms use the same window size throughout the image to produce a decision map; furthermore, a different decision map is produced for each pair of images. MF-AW determines the largest possible window size delimited by the edges of the decision map, which are improved using an iterative process. The execution time is improved using integral images, binary search, and parallel programming; as a result, the fused image is obtained in tenths of a second. Quantitative and qualitative measures indicate that the results obtained with this algorithm outperform the state of the art in terms of both accuracy and execution time.

Kuwahara filter is a smoothing filter used in image processing for adaptive noise reduction that has the ability to preserve object edges. Applications for this filter exist in fields such as medical imaging and artistic imaging. However, it has a very high computational cost, especially when filter size is large. In this paper, we propose an efficient algorithm to accelerate this filter regardless of filter size. Our method uses Summed-area Table (SAT) to gain fast computation of mean and variance values in Kuwahara filter. After that, three acceleration methods, namely caching mean and variance values, memory access optimization and flexible-format SATs are proposed to optimize the SAT-based Kuwahara filter. The experiments show that our method achieves the lowest possible big-O time complexity and performs at around the same level regardless of filter size, while producing the exact same output image as the original method. The achieved speedup ratio grows quadratically with the filter size, ranging from 10x to 1000x and even more. As a result, our optimized filter can run at a high frame rate even when the filter is large.

Non-uniform light distribution problems often arise in the real world images taken by digital device. In this paper, we propose a fast and efficient technique for color image enhancement using fast and adaptive bi-dimensional empirical mode decomposition. This method removes non-uniform light information from the illumination plane of color image efficiently. And then, we apply a brightness correction method to compensate the light information. The experimental results show that the proposed approach can effectively enhance the color image. Besides, this method requires less computing resource and is well suitable for implementing on the real-time system.