High Frequency Sub-band Images Research Articles

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Speckle Noise Reduction in Ultrasound Images

Abstract: Ultrasound Imaging is used to examine various organs in the Human Body. However, in the process of obtaining a Ultrasound Image, Speckle noise is generated due to backscattered echo signals. So, a noise reduction method is required. To improve the speckle noise reduction, we propose a novel algorithm using characteristics of speckle noise and filtering methods based on Speckle Reducing Anisotropic Diffusion (SRAD) filtering, Discrete Wavelet Transform (DWT), Weighted Guided Image Filtering (WGIF) and Gradient Domain Guided Image Filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency subband images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

Micro-image definition evaluation using multi-scale decomposition and gradient absolute value

Aimed at the problem of automatic focus and image system quality evaluation in microscopy imaging, a micro-image definition evaluation method is presented by combining multi-scale decomposition tools and absolute gradient operators. The multiscale and multidirectional non-subsampled Shearlet transform is utilized to decompose the input micro image into a low frequency sub-band image and a number of high frequency sub-band images. Combined with the anti-noise threshold setting, the gradient absolute sum values of each sub-band image were calculated. By using the different effects of image sharpness on the low-frequency and high-frequency sub-band coefficients, the ratio of the high-frequency to low-frequency gradient absolute value operator was taken as the final evaluation value of the microscopic image sharpness. The simulation experiment and actual experiments were carried out and the experimental results illustrated that the proposed approach has good monotonicity and anti-noise characteristics. Compared with other classic evaluation algorithms, the presented method obtained superior performance on sensitivity, stability and robustness. It has very good practical application values.

Estimation of the Hydrophobicity of a Composite Insulator Based on an Improved Probabilistic Neural Network

The estimation of hydrophobicity for composite insulators is of great importance for the purpose of predicting the surface degradation. The hydrophobic image is firstly decomposed by the 2-level wavelet, along with the multi-Retinex algorithm in this paper. The processed low frequency sub-band and high frequency sub-band images are then reconstructed. The 3 × 3 Sobel operator is performed to measure the basic spatial gradient in four directions, including the horizontal direction, the diagonal direction, and then the vertical direction. The shape factor, the area ratio of the largest water droplet, and the coverage rate of the water droplet are selected as the feature parameters and input into the classification network that has been trained to do the hydrophobic level recognition. The effect of the different expansion speed on the desired learning results is discussed. The threshold plays a key role in image processing. Considering that the difference between the water droplet edge and the composite insulator surface is relatively small, the asymptotic semi-soft threshold function is used in pretreatment, whereas the adaptive two-dimensional Otsu’s method is used in image segmentation. The experimental results show that the proposed method has high recognition accuracy up to 94.8% for a diversity of images, and it is superior to the improved Shape Factor Method, the Multi-fractal Method, and the RBF Neural Network.

Image resolution Enhancement by using undecimated Wavelet Decomposition

A method of an image resolution enhancement system based on the Undecimated Wavelet Transform (UWT), for the interpolation of the high frequency sub band images from the original input images. First the edges enhancement process is done during the intermediate stages of the wavelet transform. Then the UWT is applied, for the decomposition of different sub-band from the input image. Then the interpolation process is done between the input images and the high frequency sub-band images. This high frequency sub-band is altered by using the UWT algorithm based high frequency sub-bands. Finally the Inverse UWT (IUWT) algorithm is performed to add all the sub-bands for generating the new high resolution image which is called as the enhanced high resolution image. The Peak to Signal Noise Ratio (PSNR) is found in order test out resolution enhancement method performance.

A Face Recognition Algorithm Based on Improved Contourlet Transform and Principle Component Analysis

As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition.

A DWT-SWT based Image Super Resolution with Multi Surface Fitting

paper, an image super-resolution technique is proposed which is based on interpolation of high frequency sub-band images obtained by Discrete Wavelet Transform on input image. In those sub-bands edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. The wavelet transform is applied in order to decompose image into different sub-bands. In those sub-bands, the high frequency sub-bands are interpolated and then these estimated high frequency sub-bands are modified by using high frequency sub-bands obtained through SWT. These all sub- bands are fused to generate a new high resolution by using inverse Wavelet transform techniques. The proposed results depict the conventional and state of art image resolution enhancement techniques.

Stereo matching cost computation based on nonsubsampled contourlet transform

A new matching cost computation method based on nonsubsampled contourlet transform (NSCT) for stereo image matching is proposed in this paper. Firstly, stereo image is decomposed into high frequency sub-band images at different scales and along different directions by NSCT. Secondly, by utilizing coefficients in high frequency domain and grayscales in RGB color space, the computation model of weighted matching cost between two pixels is designed based on the gestalt laws. Lastly, two types of experiments are carried out with standard stereopairs in the Middlebury benchmark. One of the experiments is to confirm optimum values of NSCT scale and direction parameters, and the other is to compare proposed matching cost with nine known matching costs. Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.

Super Resolution Image Generation Using Wavelet Domain Interpolation With Edge Extraction via a Sparse Representation

This letter addresses the problem of generating a super-resolution (SR) image from a single low-resolution (LR) input image in the wavelet domain. To achieve a sharper image, an intermediate stage for estimating the high-frequency (HF) subbands has been proposed. This stage includes an edge preservation procedure and mutual interpolation between the input LR image and the HF subband images, as performed via the discrete wavelet transform (DWT). Sparse mixing weights are calculated over blocks of coefficients in an image, which provides a sparse signal representation in the LR image. All of the subband images are used to generate the new high-resolution image using the inverse DWT. Experimental results indicated that the proposed approach outperforms existing methods in terms of objective criteria and subjective perception improving the image resolution.

A Video Super Resolution Algorithm Based on Wavelet Coefficient

In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

Image Resolution Enhancement by Using Stationary and Discrete Wavelet Decomposition

This work proposed an image resolution enhancement technique which is based on the interpolation of the high frequency subbands obtained by DWT. The proposed technique uses DWT to decompose an image into different subbands, and then the high frequency subband images have been interpolated. The interpolated high frequency subband coefficients have been corrected by using the high frequency subbands achieved by SWT of the input image. An original image is interpolated with half of the interpolation factor used for interpolation the high frequency subbands. Afterwards all these images have been combined using IDWT to generate a super resolved imaged. The proposed technique has been tested on well-known benchmark images, where their PSNR, Mean Square Error and Entropy results show the superiority of proposed technique over the conventional and state-of-art image resolution enhancement techniques.

An Efficient Bayesian Approach to Face Recognition Based on Wavelet Transform

Face recognition plays a vital role in identification and for authentication purpose, in our day to day lives. In real time, this identification must be liable, perfect and faster. In this paper, face recognition approach using wavelet transform and Bayesian model is proposed, where the images have been recognized with minimal time and improved efficiency. First the original image is decomposed into Low frequency and High frequency sub-band images by applying wavelet transform and then the Bayesian approach is used to find the intrapersonal difference between the images. The Principal Component Analysis algorithm is used to compute the eigenvector space of the face. The face recognition rate was gained through similarity measure using whitening transform. In this paper, the performance of the proposed method is verified using the databases of face images, taken at different times, varying the lighting, facial expressions and facial details. In the proposed approach, the recognition rate is highly improved and hence it yields better classification compared to the existing approaches.

IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Image denoising method based on bidimensional empirical mode decomposition and mean filtering

A new image-denoising method was proposed.Noised image was decomposed to a series of sub-band images by Bimensional Empirical Mode Decomposition(BEMD).High frequency sub-band images were denoised by mean filtering using different filter templates,and low frequency approximation image remained unchanged in this process.Then the denoised-image was obtained by composing the low frequency approximation images and the high frequency detailed images with mean filtering.Experimental result shows that the noise is effectively removed and the detail of the image is well preserved.This method has better denoising effect than single BEMD method and mean filtering method and the method combining wavelet transform with mean filtering.