High Frequency Subbands Research Articles

Pan-sharpening via multi-scale and multiple deep neural networks

Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.

Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

Image Steganography Based on Wavelet Transform and Histogram Modification

Recently, digital communication has become a critical necessity and so the Internet has become the most used medium and most efficient for digital communication. At the same time, data transmitted through the Internet are becoming more vulnerable. Therefore, the issue of maintaining secrecy of data is very important, especially if the data is personal or confidential. Steganography has provided a reliable method for solving such problems. Steganography is an effective technique in secret communication in digital worlds where data sharing and transfer is increasing through the Internet, emails and other ways. The main challenges of steganography methods are the undetectability and the imperceptibility of confidential data. This paper presents a steganography method in frequency domain. Haar Wavelet Transform is applied for decomposition of gray level cover image into four sub-bands. The secret image is hidden in the high frequency HH sub-band after applying the histogram modification followed by scrambling process. A Histogram modification is adopted, to scale the secret image to normalize its values, that manipulates the secret image from bright image to dark. Thus the secret image becomes invisible so it can be hidden in the high frequency sub-band. Scrambling the positions can be for rows then columns, which will give strong security of the hiding process. The experimental results demonstrate the proposed method has achieved superior performance in terms of quantifiable measurement (PSNR and correlation) and in terms of visual quality. The proposed method propositions good imperceptible results and good response for against the various image attacks.

Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform

Most denoising methods are designed to deal standard images with specific type noise, which do not perform well when denoising real noisy images contain uncertain types of noise. However, underwater image is a typical uncertain type noise image. To solve this problem, this paper presents a method using spatial feature classification jointing nonsubsampled shearlet transform (NSST) for denoising uncertain type noise images. Justifiable granule is employed to solve the problem of parameter selection. The raw image was decomposed by using the NSST to get one low frequency subband and several high frequency subbands. Then, the preliminary binary map is built, the binary map is employed to decide whether a coefficient contains spatial feature or not. And we employ justifiable granule to solve the difficulty of parameter selection. The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. At last, the adaptive Bayesian threshold is used to shrink the coefficients. Simulation results show the proposed method is effective in uncertain type noise images(also have good performance in specific type noise). The method we proposed has been compared with other popular denoising methods and get excellent subjective performance and PSNR improvement.

IWT Based Iris Recognition for Image Authentication

Abstract In this paper, an efficient iris recognition algorithm is presented based on features extracted from Integer Wavelet Transform (IWT). At first, for proficient iris region localization and segmentation, Circular Hough Transform (CHT) and Relative Total Variation (RTV) model is utilized in combination. The segmented iris region is normalized and decomposed by four level IWT. 256 sub-bands are generated by applying four level Integer Wavelet Transform on the input image, out of which only 192 lower sub-bands are taken into consideration. High frequency sub-bands are ignored as it adds noise to the system and degrades the accuracy of the system. Features such as energy are extracted from each of 192 sub-bands, thus generating a 192 bit binary code. Unique iris code is computed by comparing energy of each sub-band with a pre-computed individual personalized threshold value. For image authentication, test iris image is compared with registered iris template iris code by calculating normalized hamming distance between them. IWT is preferred over Discrete Wavelet Transform (DWT), as IWT is computationally efficient since IWT compares the two integer values, while DWT may be complex in nature. The proposed algorithm provides the accuracy of 98.9% with the average execution time of 1.03 sec that is better than DWT and previous reported algorithms. False Accept Rate (FAR), False Reject Rate (FRR) and Equal Error Rate (EER) have also improved.

Efficient DWT based Fusion Algorithm for Improving Contrast and Edge Preservation

The main principle of infrared (IR) image is that it captures thermal radiation of light. The objects that are captured in low light, fog, and snow conditions can be detected clearly in IR image. But the major drawback of IR image is that it provides poor resolution and low texture information. Due to that humans are unable to understand overall scene information present in IR image. Nowadays for the detection of objects in poor weather conditions with improved texture information, the result of visible (VI) and IR image fusion is used. It is mostly used in military, surveillance, and remote sensing applications. The efficient DWT based fusion algorithm for improving contrast and edge preservation is presented in this paper. First morphology hat transform is applied on source images for improving contrast. DWT on decomposition produces low frequency and high frequency sub-bands. A novel mean weighted fusion rule is introduced in this paper for fusing low frequency sub-bands. Its aim is to improve the visual quality of final fused image. The max fusion rule has used for fusing high frequency sub-bands to improve edge information. The final fused image is reconstructed by using IDWT. In this paper, the proposed fusion algorithm has produced improved results both subjectively and as well as objectively when compared to existing fusion methods.

Composite material terahertz image fusion based on PCNN and RGEDIM under non-subsampled shearlet transform

In order to solve the problem that defects of different scales have different terahertz imaging characteristics in fiber reinforced composites, the fusion processing method of two terahertz images with complementary defect information was studied. To reduce the Gibbs phenomenon, Non-subsampled Shearlet Transform (NSST) with the property of shift- invariance was used to decompose source images and get their low-frequency subband and high frequency subband coefficients. Regional variance was used as connection strength factor of the Pulse Coupled Neural Network (PCNN) in the low frequency coefficient fusion, which is more according with human visual characteristics. In the fusion of high frequency coefficients, the Regional Gradient Energy of Direction Information Measure (RGEDIM) was introduced to extract the edge, texture and other details of the image and integrated them into the final image, the impact of noise on image fusion was reduced better. Finally, the fusion image was obtained through NSST inverse transform. The experimental results show that this method is superior to wavelet, Non-subsampled Contourlet Transform (NSCT) and traditional PCNN method, the fusion image has more mutual information and contains more original image information, all the defects of the source image can be clearly seen on the fusion image.

Sub-band-based feature fusion and hybrid fusion approaches for multimodal biometric identification

A multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of discrete wavelet transform (DWT), and principal component analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through linear discriminant analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA-based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least equal error rate (EER) of 0.086 for ORL+CASIA database.

Sub-band-based feature fusion and hybrid fusion approaches for multimodal biometric identification

A multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of discrete wavelet transform (DWT), and principal component analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through linear discriminant analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA-based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least equal error rate (EER) of 0.086 for ORL+CASIA database.

A novel Hexa data encoding method for 2D image crypto-compression

We proposed a novel method for 2D image compression-encryption whose quality is demonstrated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connected with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency sub-bands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images.

Multi-modality medical images fusion based on local-features fuzzy sets and novel sum-modified-Laplacian in non-subsampled shearlet transform domain

To obtain maximum information and key features from the source images, enhance visual quality and contrast of the fused image, and decrease computational task; an improved algorithm based on local featured fuzzy sets and NSML in NSST domain is presented here. First, by taking full advantages of NSST, two registered images of the same scene are decomposed into one Low Frequency Subbands (LFS) and several High Frequency Subbands (HFS). Then, Fuzzy Pixel-based fusion rules are applied on the LFS for computing weights of every pixel in the required fused coefficient. Weights are totally based on the local energies and entropies of the LFS. Where, fused HFS coefficients are selected by computing and comparing NSML of every HFS, to extract maximum and more useful information. Finally, inversed NSST is applied to get the required fused image. Additionally, the scheme is extended to color medical image fusion that effectively restrain color distortion and enhance visual quality. To assess the performance, several experiments were conducted on different data-sets of gray-scale and color medical images. Results obtained shows that the proposed algorithm is not only superior in edge and contour detection, visual feature and in computational performance but also presents an improvement in quantitative parameters compared to other state-of-art proposed schemes.

Infrared and visible image fusion based on intuitionistic fuzzy sets

Infrared and visible image fusion plays a vital role in target tracking, surveillance, military, etc. In this paper, we put forward a new infrared and visible image fusion method employing non-subsampled contourlet transform (NSCT) with intuitionistic fuzzy sets. We use NSCT to decompose the pre-registered test images into low frequency and high frequency sub-bands. Furthermore, we introduce a new rule based on intuitionistic fuzzy sets for high frequency sub-bands fusion, and for low frequency sub-bands, the energy-based fusion rule is used. The fused image is obtained by inverse NSCT. Experiment results show that the proposed fusion method outperforms the advanced fusion methods in terms of objective assessment and visual quality.

Invisible Medical Image Watermarking using Edge Detection And Discrete Wavelet Transform Coefficients

Protection and authentication of medical images is essential for the patient’s disease identification and diagnosis. The watermark in medical imaging application needs to be invisible and it is also required to preserve the low and high frequency features of image data which makes watermarking a difficult assignment. Within this manuscript an unseen medical image watermarking approach is projected apply edge detection in the discrete wavelet transform domain. The wavelet transform is brought into play to decay the medical picture interested in multi-frequency secondary band coefficients. The edge detection applies to high frequency wavelet group in the direction of generating the boundary coefficients used as a key. The Gaussian noise pattern is utilized as watermark as well as embedded within the edge coefficients around the edges. To add the robustness scaled dilated edge coefficient is added with the edge coefficients to generate the watermarked image. Preserving the small frequency secondary band fulfills the information requirement of the medical imaging application. At the same time as adding together the watermark during high frequency sub-bands improve the watermark invisibility. To add additional robustness the dilation is applied on the edged coefficient before being embedded with sub band coefficients. presentation of the technique is experienced on the dissimilar set of medical imagery as well as evaluation of the proposed watermarking method founds it robust not in favor of the different attacks such at the same time as filtering, turning round plus resizing. Parametric study foundation going on Mean Square Error along with Signal to Noise Ratio shows that how good method performs for invisibility.

Multifocus image fusion using convolutional neural networks in the discrete wavelet transform domain

In this paper, a novel multifocus image fusion algorithm based on the convolutional neural network (CNN) in the discrete wavelet transform (DWT) domain is proposed. The algorithm combines the advantages of spatial domain- and transform domain-based methods. The CNN is used to amplify features and generate different decision maps for different frequency subbands instead of image blocks or source images. In addition, the CNN, which can be seen as an adaptive fusion rule, replaces the traditional fusion rules. The proposed algorithm includes the following steps: first, we decompose each source image into one low frequency subband and several high frequency subbands using the DWT; second, these frequency subbands are used as input to the CNN to generate weight maps. To obtain a more accurate decision map, it is refined by a series of postprocessing operations, including the sum-modified-Laplacian (SML) and guided filter (GF). According to their decision maps, the frequency subbands are fused; finally, the fused image can be obtained using the inverse DWT. The experimental results show that our algorithm is superior to other algorithms.

Fast hierarchical wavelet-domain motion estimation for arbitrarily shaped visual objects

Motion estimation is widely used for video compression as it reduces the memory requirements of any video file while maintaining high visual quality. But the computational cost consumed by motion estimation/compensation usually accounts for more than 50%, even to 80%, of the total cost of the encoder. So researchers try to develop fast motion estimation algorithms to lower the computational complexity. Moreover, few studies have been concentrated on the efficient motion estimation of arbitrarily shaped visual objects, which particularly pays insufficient attention to boundary continuity of the motion-compensated objects as well. In this research, we present a hierarchical wavelet-domain approach to motion estimation of visual objects with arbitrary shapes. We explore the guiding role of binary alpha-plane in assisting motion estimation of visual objects, and bring forward a binary alpha-plane matching scheme. It constrains the intensity-based motion estimation into a small subset of initial candidate vectors in the search window. This both accelerates search speed and helps to produce coherent object boundary. Moreover, we employ the low-band-shift technique to eliminate the wavelet’s shift-variance effect. And based on the observation that high frequency subbands of wavelet contribute little to calculating motion vectors, a new matching function is derived for fast wavelet-domain motion estimation. Coupled with the alpha-plane matching and the devised matching function, a hierarchical low-band-based motion estimation approach is further proposed. Through executing a refined search around the initial candidate vectors using only the low frequency subband at each scale, our approach achieves a satisfying balance between prediction accuracy and computational efficiency. Experimental results show that by using the standard accuracy measurements and the benchmark video sequences, the performance of the proposed approach is close to the full search with a matching quality over 99%, and with 95.80% reduction in complexity.

A Modified DWT-SVD Algorithm for T1-w Brain MR Images Contrast Enhancement

BackgroundImage contrast enhancement is considered as the most useful technique permitting a better appearance of the low contrast images. This paper presents a modified Discret Wavelet Transform - Singular Value Decomposition (DWT-SVD) approach for the enhancement of low contrast Brain MR Images used for brain tissues exploration. MethodsThe proposed technique is processed as follows: first of all, we consider low contrast T1-weighted MRI slices as input images (A1) on which we apply General Histogram Equalization (GHE) algorithm to have equalized images referred as (A2) having zero as mean and one as variance. Secondly, by using the Discrete Wavelet Transform (DWT) algorithm, both (A1) and (A2) are divided into low and high frequency sub-bands. On the low frequency (LL1) and (LL2) sub-bands, SVD is processed in order to generate three matrix factorization (U, V and Σ) where the maximums of (U) and (V) matrix are used for the estimation of a correction coefficient (ξ). Our contribution in this paper is to estimate the new singular value matrix (New Σ) using a weighted sum of both original and equalized singular value matrix thanks to an adjustable parameter (μ) for the targeted low contrast images. This parameter, ranged between 0.05 and 0.95, is determined empirically according to the input low contrast image. Finally, the enhanced resulting image is easily reconstructed using the Inverse SVD (ISVD) and the Inverse DWT (IDWT) processes. ResultsThe database considered in our research consisted of 120 MR brain images where T1-weighted MR brain modality are selected for the contrast enhancement process. Considering the qualitative results, our proposed contrast enhancement method have shown better distinction between brain tissues and have preserved all White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF) pixel edges. In fact, histogram plots of images enhanced by proposed method covered all the gray level intensities. For the quantitative results, proposed method gives the highest PSNR, QRCM, SSIM, FSIM and EME values and the lowest AMBE values for (μ) equal to 0.65 as comparing to the rest of methods. These results signifies that proposed contrast enhancement method have provided greater image quality with preservation of image structure, feature and brightness. ConclusionProposed method improved performance of contrast enhancement image without creating unwanted artifact and without destroying image edge information or affecting the specificities of brain tissues. This is due to the use of an empirically (μ) parameter adjustable according to the input MR images. Hence, the proposed approach is appropriate for enhancing contrast of huge type of low contrast images.

Nonwovens structure measurement based on NSST multi-focus image fusion

In the digital optical microscope, the depth of field cannot clearly display all fibers in the same image, due to the thickness of nonwovens. A new multi-focus image fusion algorithm based on non-subsampled shearlet transform (NSST) is proposed to improve the quality of fused image, which realizes the fusion of a series of images taken from the same perspective and makes all fibers clearly within a single image. The rule of large absolute value is used to fuse the high frequency sub-band and the rule of large regional variance is used to fuse the low frequency sub-band. Comparing the method with other methods, the superiority of the method can be seen from several indicators of image quality evaluation. Based on the fused image, the diameter and orientation are measured by Hough transform and image preprocessing, and automatic measurement is realized. The porosity is measured by identifying pores, which is fast and convenient. Experiments show that the measurement of nonwoven fabric structure can be quickly achieved based on image processing.

A New Multi-Focus Image Fusion Algorithm and Its Efficient Implementation

Wavelet transform using Haar filter is a fast process for decomposing an image into low and high frequency sub-bands, which is an important step in multi-focus image fusion. This transformation alone is usually not sufficient to produce satisfying fused images, and therefore additional tools like focused region decision map and feature extraction are needed to enhance the fusion quality. In this paper, we propose a multi-focus image fusion algorithm that is suitable for efficient hardware implementation. The algorithm speed is significantly improved by utilizing Haar wavelet and computationally simple fusion rules. To further improve the circuit power and delay, we limit the logic operations to additions, subtractions, and multiplications only. Experiments on different benchmarks demonstrate that our proposed algorithm can reduce the fusion time by up to 99.97% compared with the state-of-the-art competing methods without compromising the fusion quality. In addition, the hardware implementation of the proposed algorithm reduces the circuit area and delay by 79.5% and 92.5%, respectively, when compared to the most competitive algorithm in this paper while maintaining similar fusion quality.

A low complexity wavelet-based blind image quality evaluator

The aim of blind image quality assessment (BIQA) methods is to evaluate the perceptual quality of a distorted image without any prior information regarding its reference image. Although some impressive image quality metrics have been proposed, due to the complexity of the human visual system and the lack of a reference image, designing an image quality metric which accurately predicts human judgments is still a challenging issue. In this paper, a low complexity wavelet-based image quality assessment is proposed. Firstly, the interaction of fine and coarse details of the image, which is extracted by Haar wavelet, is analyzed. In the proposed approach, the joint statistics of two normalized high frequency subbands which indicate coarse and fine structures is utilized for extracting features. Actually, analyzing the relation between image details of different granularities is the main idea of the proposed method. After feature extraction phase, support vector regression (SVR) is adopted in order to provide a quality score. Experimental results show the effectiveness of the proposed low complexity approach.

Medical Image Fusion via PCNN Based on Edge Preservation and Improved Sparse Representation in NSST Domain

Medical image fusion integrates image features of different modalities to provide comprehensive information for clinical diagnosis, treatment planning, and image-guided surgery. The information of the fused image is richer and clearer, which makes up for the defect of the single-mode medical image and retains the characteristic information of the source image. This paper proposes a novel multi-modality medical image fusion method based on gray medical images and color medical images. In the proposed method, the nonsubsampled shearlet transform (NSST) method is first used to decompose the source image into a low frequency sub-band and several high frequency sub-bands. The improved sparse representation is utilized to fuse the low frequency sub-band, which can remove the detail features through the sobel operator and the guided filter to improve the ability to preserve energy effectively. Meanwhile, the high frequency sub-bands are fused by a pulse coupled neural network (PCNN) based on edge preservation. This method fully considers the imaging characteristics of different medical modalities, which can process edge information well and process image details better. Finally, the fused low frequency sub-band and high frequency sub-bands are inversely transformed to obtain the final fused image. Seven different categories of medical images and seven fusion methods are used to verify the effectiveness of the proposed method. In addition, the medical images of three different modalities are merged to testify the influence of the fusion sequence. The experimental results show that the proposed method is superior to existing state-of-art methods in subjective visual performance and objective evaluation.