Analysis of image compression methods based on wavelet transforms for maritime applications

The purpose of the article is to develop recommendations for choosing image compression method based on wavelet transformation, depending on image type, quality and compression requirements. Among the wavelet image compression methods, Embedded Zerotree Wavelet coder (EZW) and Set Partition In Hierarchical Trees (SPIHT) are considered, and the Haar wavelet and wavelet transformation in the oriented basis with the first, third, fifth and seventh decomposition levels is used as the base wavelet transform. These compression methods were compared with each other and with the standard JPEG method on the following parameters: mean square error, maximum error, peak to noise ratio, number of bits per pixel, compression ratio, and image size. The proposed methods can be successfully applied in the transmission of seabed relief images obtained from satellites or sea buoys.

Digital Video Image Preprocessing Algorithm Based on Embedded System

To solve the massive noise contained in the images acquired under low illumination, we designed a digital video image Preprocessing device with the denoising function. Based on the embedded CPU and operating system, video images are acquired by the camera. The noise contained in the video images is filtered by the improved median filtering algorithm and wavelet image denoising. Subsequently, the images are transmitted through USB and network interface, and the storage function of image files is implemented. The device can remove the noise contained in videos effectively, which is conducive to performing more advanced processing on the images.

A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization

A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the continuous wavelet transform with damage frequency band information to generate the defect signature wavelet image (DSWI), which describes the acoustic emission signal in time-frequency-domain, reduces the nonstationary effect in the signal, shows discriminate pattern visualization for different types of faults, and associates with the defect signature of bearing faults. Using the resultant DSWI, the deep convolution neural network (DCNN) architecture is designed to identify the fault in the bearing. To evaluate the proposed algorithm, the performance of this technique is scrutinized by a series of experimental tests acquired from a self-designed testbed and corresponding to different bearing conditions. The performance from the experimental dataset demonstrates that the suggested methodology outperforms conventional approaches in terms of classification accuracy. The result of combining the DCNN with DSWI input yields an accuracy of 98.79% for classifying multiple bearing defects.

Estimation of Coastal Bathymetry Using Wavelets

When waves propagate in coastal areas at depths lower than one half the wavelength, they exhibit a different signature at the sea surface and the observed wavelength pattern enables inferring bathymetries. Commonly, a spectral analysis using the fast Fourier transform (FFT) is employed to derive wavelength and wave direction of swell waves, in nearshore regions. Nevertheless, it is recognized that this method presents limitations, particularly regarding depth inversion limits that do not allow obtaining bathymetric data close to the shoreline. This work explores a wavelet spectral analysis to obtain bathymetric data. This new imaging methodology is applied over different seafloors with 2D and 3D features such as longshore bars or headlands. The synthetic images of the water surface are generated from a numerical Boussinesq-type model that simulates the propagation of both regular and irregular waves. The spectral analysis is carried to estimate the water depths, which are validated with the model’s bathymetry. Wavelet image processing methodology shows very positive results, revealing the capabilities of this new methodology to map shallow marine environments.