High-frequency Components Of Image Research Articles

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform.

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.

An Adaptive Two-Layer Light Field Compression Scheme Using GNN-Based Reconstruction

As a new form of volumetric media, Light Field (LF) can provide users with a true six degrees of freedom immersive experience because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. But uncompressed LF data is too large for network transmission, which is the reason why LF compression has become an important research topic. One of the more recent approaches for LF compression is to reduce the angular resolution of the input LF during compression and to use LF reconstruction to recover the discarded viewpoints during decompression. Following this approach, we propose a new LF reconstruction algorithm based on Graph Neural Networks; we show that it can achieve higher compression and better quality compared to existing reconstruction methods, although suffering from the same problem as those methods—the inability to deal effectively with high-frequency image components. To solve this problem, we propose an adaptive two-layer compression architecture that separates high-frequency and low-frequency components and compresses each with a different strategy so that the performance can become robust and controllable. Experiments with multiple datasets 1 show that our proposed scheme is capable of providing a decompression quality of above 40 dB, and can significantly improve compression efficiency compared with similar LF reconstruction schemes.

Ultrasound Image Despeckling and Enhancement using Modified Multiscale Anisotropic Diffusion Model in Non-Subsampled Shearlet Domain

Abstract Ultrasound imaging is undoubtedly the most used imaging modality for diagnostic purposes. Unfortunately, it is accompanied by speckle which can degrade texture information by obscuring fine details like boundaries and edges. This work presents a method for despeckling ultrasound images by treating them with multiscale modified speckle reduction anisotropic diffusion model and Non-Subsampled shearlet transform (NSST). The method involves division of images using a non-subsampled Laplacian pyramid. This results in low and high frequency image components. Modified anisotropic diffusion is used on the low frequency part. The high frequency component, as subjected to shearlet function, generates noisy coefficients in various directions. These coefficients are further subjected to NSST thresholding. The denoised low and high frequency image components are then recombined to obtain the enhanced image. This multidimensional and multidirectional method improves the qualitative characteristics of ultrasound images by not just removing speckle noise but also by preserving edges, thus resulting in effective image enhancement. Performance of the method is analysed on synthetic and real medical ultrasound images. Results reveal that the proposed method exceeds the state-of-the-art methods in the context of edge preservation and structural similarities, and thus, it is an effective aid to radiologists in their clinical diagnosis by providing an enhanced denoised image.

Multilevel Wavelet Decomposition Based Harris Corner Detection Algorithm for Remote-sensing Image

Because of the interference of the noise and the subtle texture, the Harris algorithm will generate much more false corners. An improved Harris algorithm based on multilevel wavelet decomposition is proposed. By multilevel wavelet decomposition, the high frequency components of image are extracted. These high frequency components with different scales are parsed, and the noises and subtle textures are suppressed while the original image edge features are kept. The final experiment shows that the improved corner detection algorithm can effectively reduce the number of false corners.

Wavelet Packet and Hybrid Filter Based Digital Watermarking

In this paper, a new watermark algorithm that based on wavelet packet transform and hybrid filter is proposed for watermark of digital images, which include many high frequency components. The proposed watermark algorithm is used in the high frequency component of image and applies watermark to the overall subband that include the lowest frequency band. And the watermark is embedded on input image and hybrid filtering concept has been adopted to remove the noise that was added to the original image in watermark embedding stage. The quality of the watermarked image has been analyzed with PSNR. From the simulation results, the proposed algorithm shows better invisibility and robustness performance compare with conventional watermark methods. Especially, it demonstrates better robustness for high image compression in the remote sensing images application.

Dictionary Learning for Scalable Sparse Image Representation with Applications

This paper introduces a novel design for the dictionary learning algorithm, intended for scalable sparse representation of high motion video sequences and natural images. The proposed algorithm is built upon the foundation of the K-SVD framework originally designed to learn non-scalable dictionaries for natural images. Proposed design is mainly motivated by the main perception characteristic of the Human Visual System (HVS) mechanism. Specifically, its core structure relies on the exploitation of the high-frequency image components and contrast variations in order to achieve visual scene objects identification at all scalable levels. Proposed design is implemented by introducing a semi-random Morphological Component Analysis (MCA) based initialization of the K-SVD dictionary and the regularization of its atom’s update mechanism. In general, dictionary learning for sparse representations leads to state-of-the-art image restoration results for several different problems in the field of image processing. In experimental section we show that these are equally achievable by accommodating all dictionary elements to tailor the scalable data representation and reconstruction, hence modeling data that admit sparse representation in a novel manner. Performed simulations include scalable sparse recovery for representation of static and dynamic data changing over time (e.g., video) together with application to denoising and compressive sensing.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality. References R. C. Gonzalez, R. E. Woods, Digital Image Processing . Third Edition. Prentice Hall, New Jersey, USA, 2007. C. S. Panda, S. Patnaik, Filtering corrupted image and edge detection in restored grayscale image using derivative filters, International Journal of Image Processing , 3(3):105–119, 2009. http://www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/Issue3/IJIP-28.pdf Y. Zhang, Y. Pu, J. Zhou, Construction of Fractional differential Masks Based on Riemann–Liouville Definition, Journal of Computational Information Systems , 6(10):3191–3199, 2010. http://www.jofcis.com/publishedpapers/2010_6_10_3191_3199.pdf F. Liu, P. Zhuang, V. Anh, I. Turner, K. Burrage, Stability and convergence of the difference methods for the space–time fractional advection–diffusion equation, J. Appl. Math. Comput. , 191:12–21, 2007. doi:10.1016/j.amc.2006.08.162 Q. Yu, F. Liu, I. Turner and K. Burrage, Stability and convergence of an implicit numerical method for the space and time fractional Bloch–Torrey equation, Phil Trans R Soc A , 371(1990):20120150, 2013. doi:10.1098/rsta.2012.0150 E. Sejdic, I. Djurovic, L. Stankovic, Fractional Fourier transform as a signal processing tool: An overview of recent developments, Signal Processing , 91(6):1351–1369, 2011. doi:10.1016/j.sigpro.2010.10.008 B. Pesquet-Popescu and J. L. Vehel, Stochastic fractal models for image processing, IEEE Signal Processing Magazine , 19(5):48–62, 2002. doi:10.1109/MSP.2002.1028352 B. Mathieu, P. Melchior, A. Oustaloup, Ch. Ceyral, Fractional differentiation for edge detection, Signal Processing , 83(11):2421–2432, 2003. doi:10.1016/S0165-1684(03)00194-4 C. B. Gao, J. L. Zhou, J. R. Hu, F. N. Lang, Edge detection of colour image based on quaternion fractional differential, IET Image Processing , 5(3):261–272, 2011. http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2009.0409 C. B. Gao, J. L. Zhou, X. Q. Zheng, F. N. Lang, Image enhancement based on improved fractional differentiation, Journal of Computational Information Systems , 7(1):257–264, 2011. http://www.jofcis.com/publishedpapers/2011_7_1_257_264.pdf Y. Pu, J. Zhou and X. Yuan, Fractional differential mask: A fractional differential–based approach for multiscale texture enhancement, IEEE Transactions on Image Processing , 19(2):491–511, 2010. doi:10.1109/TIP.2009.2035980 M. D. Ortigueira, Riesz potential operators and inverses via fractional centred derivatives, International Journal of Mathematics and Mathematical Sciences , 2006:48391, 2006. doi:10.1155/IJMMS/2006/48391

COMPARATIVE ANALYSIS OF SATELLITE IMAGE PRE-PROCESSING TECHNIQUES

Satellite images are corrupted by noise in its acquisition and transmission. The removal of noise from the image by attenuating the high frequency image components, removes some important details as well. In order to retain the useful information and improve the visual appearance, an effective denoising and resolution enhancement techniques are required. In this research, Hybrid Directional Lifting (HDL) technique is proposed to retain the important details of the image and improve the visual appearance. The Discrete Wavelet Transform (DWT) based interpolation technique is developed for enhancing the resolution of the denoised image. The performance of the proposed techniques are tested by Land Remote-Sensing Satellite (LANDSAT) images, using the quantitative performance measure, Peak Signal to Noise Ratio (PSNR) and computation time to show the significance of the proposed techniques. The PSNR of the HDL technique increases 1.02 dB compared to the standard denoising technique and the DWT based interpolation technique increases 3.94 dB. From the experimental results it reveals that newly developed image denoising and resolution enhancement techniques improve the image visual quality with rich textures.

Developing an efficient technique for satellite image denoising and resolution enhancement for improving classification accuracy

Satellite images are corrupted by noise during image acquisition and transmission. The removal of noise from the image by attenuating the high-frequency image components removes important details as well. In order to retain the useful information, improve the visual appearance, and accurately classify an image, an effective denoising technique is required. We discuss three important steps such as image denoising, resolution enhancement, and classification for improving accuracy in a noisy image. An effective denoising technique, hybrid directional lifting, is proposed to retain the important details of the images and improve visual appearance. The discrete wavelet transform based interpolation is developed for enhancing the resolution of the denoised image. The image is then classified using a support vector machine, which is superior to other neural network classifiers. The quantitative performance measures such as peak signal to noise ratio and classification accuracy show the significance of the proposed techniques.

Efficient analysis of hybrid directional lifting technique for satellite image denoising

Satellite images are often corrupted by noise in the acquisition and transmission process. While removing noise from the image by attenuating the high frequency image components, it removes some important details as well. In order to improve the visual appearance and retain the useful information of the images, an effective denoising technique is required to reduce the noise level. For denoising, many researches exploit the directional correlation in either spatial or frequency domain. However, the orientation estimation for directional correlation becomes inefficient and error prone in noised circumstances. This paper proposes a new hybrid directional lifting (HDL) technique for image denoising that involves pixel classification and orientation estimation, along with adding small amount of noise, in order to improve the performance efficiency of the technique. Experimental results show that the HDL technique improves both peak signal to noise ratio and visual quality of images with rich textures.

Multi-resolution fuzzy clustering approach for image-based particle characterization for particle systems

Online characterization of particles is an important step for maintaining desired product quality in particulate processes. Direct real-time image analysis is a promising method for monitoring particle systems, and is becoming increasingly more attractive due to availability of high speed imaging devices and equally powerful computers. Performing image segmentation (separation of objects (particles) within one image) accurately becomes a key issue in particle image analysis. This paper presents a novel technique based on combining wavelet transform and Fuzzy C-means Clustering (FCM) for particle image segmentation. Through performing wavelet transform on images, the noise and high frequency components of images can be eliminated and the textures and features can be obtained. FCM is then used to divide data into two clusters to separate touching objects. To quantitatively evaluate this method, a case study involving a particle image is investigated. The procedure of selecting optimum wavelet function and decomposition level for this image is presented. ‘Fuzzy range’ is used as a derived feature for segmentation. The number of particles, particle equivalent diameters, and size distribution before and after partition are discussed. The results show that this method is effective and reliable.

Noise insensitive focus value operator for digital imaging systems

Most auto-focusing algorithms based on focus values which are calculated with high frequency image components are very sensitive to noise factor. The noise under low illumination conditions generally degrades the auto-focusing performance. In this paper, we propose an operator to determine the noise insensitive focus value for digital imaging systems. The proposed algorithm uses spatially adapted weights based on the local statistics of the image and the noise factors. Experiments conducted with and without noise factors demonstrate the high performance of the proposed method.

Multi-resolution Fuzzy Clustering Approach for Image-Based Particle Characterization

This paper presents a novel technique based on combing wavelet transform and Fuzzy C-means Clustering (FCM) for particle image analysis. Through performing wavelet transform on images, the noise and high frequency components of images can be eliminated and the textures and features can be obtained. FCM is then used to divide data into two clusters to separate touching objects. To quantitatively evaluate this method, a case study involving a particle image is investigated. The procedure of selecting optimum wavelet function and decomposition level for this image is presented. 'Fuzzy range' is used as a derived feature for segmentation. The amounts of particles, particle equivalent diameters, and size distribution before and after partition are discussed. The results show that this method is effective and reliable.

正規化順位法による解像度や表示色が異なる医用画像表示用液晶ディスプレイの評価

The purposes of this study were to evaluate the image quality of five types of liquid-crystal display (LCD) monitors by utilizing the normalized-rank approach and to investigate the effect of LCD monitor specifications, such as display colors, luminance, and resolution, on the evaluators' ranking. The LCD monitors used in this study were 2, 3 and 5 mega-pixel monochrome LCD monitors, and 2 and 3 mega-pixel color LCD monitors (Eizo Nanao Corporation). All LCD monitors were calibrated to the grayscale standard display function (GSDF) with different maximum luminance (recommended luminance) settings. Also, four kinds of radiographs were used for observer study based on the normalized-rank approach: three adult chest radiographs, three pediatric chest radiographs, three ankle joint radiographs, and four double-contrasted upper gastrointestinal radiographs. Ten radiological technologists participated in the observer study. Monochrome LCD monitors exhibited superior ranking with statistically significant differences (p<0.05) compared to color LCD monitors in all kinds of radiographs. The major difference between monochrome and color monitors was luminance. Therefore, it is considered that the luminance of LCD monitors affects observers' evaluations based on image quality. Moreover, in the case of radiographs that include high frequency image components, the monitor resolution also affects the evaluation. In clinical practice, it is necessary to optimize the luminance and choose appropriate LCD monitors for diagnostic images.

Sharpness preserving image enlargement by using self-decomposed codebook and Mahalanobis distance

We propose an image enlargement method preserving perceptual sharpness, which is achieved by augmenting a low resolution image with high-frequency components from a given image itself. The estimation of high-frequency image components is performed by a codebook built by a decomposition of the given image, i.e. a self-decomposed codebook. The rational that is exploited in this approach is the shape-invariant properties of edges across scales. As a distance measure for matching from the codebook, we employ the Mahalanobis distance which is a local distance measure incorporating pixel correlation. The effectiveness of the proposed method is verified by some image enlargement experiments. Consequently, the experimental results show that the performance of the proposed method is superior to other conventional image enlargement methods objectively and subjectively.

Image super-resolution based on local self-similarity

A new image super-resolution method based on a codebook mapping is proposed. The codebook mapping represents the internal relationship between low-and high-frequency image components, and is used for the estimation of highfrequency image components lost in the sampling process. In the proposed method, codebooks are first generated by the low-and high-frequency image components of the original image itself that is to be processed. Then, a resultant superresolution image, that is, an enlarged image, is obtained by combining the estimated high-frequency image components with the low-frequency ones obtained by a typical interpolation-based method. The effectiveness of the proposed method is verified by comparing it against some conventional methods.

An adaptive color plane interpolation method based on edge detection

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional (1D) gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.

Image enlargement based on self-produced codebook

A new image enlargement method for the estimation of high frequency image components lost in the sampling process is proposed. The method uses a codebook consisting of the low- and high-frequency components of an original image itself that is to be enlarged. The effectiveness of the proposed method is verified by comparing with conventional methods.

Spatial frequency analysis of optical lithography resolution enhancement techniques

A consistent frequency-space analysis of the effects of optical lithography resolution enhancement techniques including optical proximity correction, off-axis illumination, phase-shift masks, and imaging interferometric lithography is presented. The improvements for each of these techniques are directly related to the enhanced frequency-space coverage afforded. Optical proximity correction extends the frequency coverage out to ∼1.2–1.3×NA/λ where NA is the optical system numerical aperture and λ the exposure wavelength enabling κ1’s of ∼0.45 in the context of the usual Rayleigh resolution equation CD=κ1λ/NA. There are many possible configurations for off-axis illumination. For a quadrupole oriented at 45° to the (x, y) pattern axes, the maximum spatial frequency is extended to 2NA/λ or κ1∼0.43. Adding pupil plane filters to ensure a uniform modulation transfer function and orienting the quadrupole along the pattern axes allows extension to frequencies of 2NA/λ or κ1∼0.3. Phase shifts at the mask plane emphasize the high frequency image components by increasing the importance of the quadratic imaging terms and allow frequencies to 2NA/λ and κ1’s out to ∼0.35. Imaging interferometric lithography further extends the frequency coverage out to either (1+NA)/λ or 3NA/λ depending on the details with corresponding κ1 s of ∼0.23–0.2.

Acceleration and filtering in the generalized Landweber iteration using a variable shaping matrix

The generalized Landweber iteration with a variable shaping matrix is used to solve the large linear system of equations arising in the image reconstruction problem of emission tomography. The method is based on the property that once a spatial frequency image component is almost recovered within in in the generalized Landweber iteration, this component will still stay within in during subsequent iterations with a different shaping matrix, as long as this shaping matrix satisfies the convergence criterion for the component. Two different shaping matrices are used: the first recovers low-frequency image components; and the second may be used either to accelerate the reconstruction of high-frequency image components, or to attenuate these components to filter the image. The variable shaping matrix gives results similar to truncated inverse filtering, but requires much less computation and memory, since it does not rely on the singular value decomposition.