Bandpass Images Research Articles

Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across hardware. This paper proposes a DL model that replicates the fine-tuned BMode signal processing chain of a high-end US system and explores the potential of using it with a different probe and a lower-end system. A deep neural network was trained in a supervised manner to map raw beamformed in-phase and quadrature component data into processed images. The dataset consisted of 30,000 cardiac image frames acquired using the GE HealthCare Vivid E95 system with the 4Vc-D matrix array probe. The signal processing chain includes depth-dependent bandpass filtering, elevation compounding, frequency compounding, and image compression and filtering. The results indicate that a lightweight DL model can accurately replicate the signal processing chain of a commercial scanner for a given application. Evaluation on a 15 patient test dataset of about three thousand image frames gave a structural similarity index measure of 98.56 ± 0.49. Applying the DL model to data from another probe showed equivalent or improved image quality. This indicates that a single DL model may be used for a set of probes on a given system that targets the same application, which could be a cost-effective tuning and implementation strategy for vendors. Further, the DL model enhanced image quality on a Verasonics dataset, suggesting the potential to port features from high-end US systems to lower-end counterparts.

Multi-Focus Image Fusion for Full-Field Optical Angiography.

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations.

A Spatial Near-Field Clutter Reduction Filter Preserving Tissue Speckle in Echocardiography.

Near-field (NF) clutter in echocardiography is depicted as a diffuse haze hindering the visualization of the myocardium and the blood-pool, thereby degrading its diagnostic value. Several clutter filters have been developed, which are limited in patients with contraction motion and rhythm anomalies, and in 3-D ultrasound (US). This study introduces a new NF clutter reduction method, which preserves US speckles required for strain imaging. The filter developed detects the NF clutter region in the spatial frequency domain. The filter employs an oriented, multiscale approach, and assumes the NF clutter to be predominantly present in the highest and lowest bandpass images. These bandpass images were filtered, whilst sparing features in the myocardium and NF clutter-free regions. The performance of the filter was assessed in a volunteer study, in ten 3-D apical and parasternal view acquisitions, and in a retrospective clinical study composed of 20 cardiac patients with different indications for echocardiography. The filter reduced NF clutter in all data sets, whilst preserving all or most of the myocardium. Additionally, it demonstrated a consistent enhancement of image quality, with an increase in contrast of 4.3 dB on average, and generated a clearer myocardial boundary distinction. Furthermore, the speckles were preserved according to the quality index based on local variance, the structural similarity index method, and normalized cross correlation values, being 0.82, 0.92, and 0.95 on average, respectively. Global longitudinal strain measurements on NF clutter reduced images were improved or equivalent compared to the original acquisitions, with an average increase in strain signal-to-noise ratio of 34%.

Perceptually guided contrast enhancement based on viewing distance.

We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.

A Fuzzy Approach to Recognize Face Using Contourlet Transform

Face recognition addresses identification, verification, and authentication in biometric-based security systems. This work enhances the contrast and edges in face images and recognizes the face using contourlet transform and fuzzy rules. Contourlet transformed image provides multiscale and directional information. The transformed image is divided into low-pass image (low-frequency image) and band-pass image (high-frequency image). The low-pass image is enhanced using fuzzy-based histogram specification since it deals with contrast. Band-pass image contains detailed information about the edges of the image and are enhanced using fuzzy rules and morphological gradient operators. The proposed system achieves the accuracy rate of 99.81% and 99.35% on Yale-B and JAFEE dataset, respectively, which is better than the existing curvelet and wavelet transform-based recognition. The incorporation of fuzzy rules enhances the mean intensity value of the edges to 34.19, which is better than Canny, Sobel, Prewitt, Robert and Laplacian edge detection techniques. Finally Discriminant Correlation Analysis (DCA) feature level fusion is applied to fuse enhanced edge intensities and histogram features for Support Vector Machine (SVM) classification.

Multivariate Statistical Approach to Image Quality Tasks

Many existing Natural Scene Statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher-order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, that facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.

Towards a Closed Form Second-Order Natural Scene Statistics Model.

Previous work on natural scene statistics (NSS)-based image models has focused primarily on characterizing the univariate bandpass statistics of single pixels. These models have proven to be powerful tools driving a variety of computer vision and image/video processing applications, including depth estimation, image quality assessment, and image denoising, among others. Multivariate NSS models descriptive of the joint distributions of spatially separated bandpass image samples have, however, received relatively little attention. Here, we develop a closed form bivariate spatial correlation model of bandpass and normalized image samples that completes an existing 2D joint generalized Gaussian distribution model of adjacent bandpass pixels. Our model is built using a set of diverse, high-quality naturalistic photographs, and as a control, we study the model properties on white noise. We also study the way the model fits are affected when the images are modified by common distortions.

Combustion stratification study of partially premixed combustion using Fourier transform analysis of OH* chemiluminescence images

A relatively high level of stratification (qualitatively: lack of homogeneity) is one of the main advantages of partially premixed combustion over the homogeneous charge compression ignition concept. Stratification can smooth the heat release rate and improve the controllability of combustion. In order to compare stratification levels of different partially premixed combustion strategies or other combustion concepts, an objective and meaningful definition of “stratification level” is required. Such a definition is currently lacking; qualitative/quantitative definitions in the literature cannot properly distinguish various levels of stratification. The main purpose of this study is to objectively define combustion stratification (not to be confused with fuel stratification) based on high-speed OH* chemiluminescence imaging, which is assumed to provide spatial information regarding heat release. Stratification essentially being equivalent to spatial structure, we base our definition on two-dimensional Fourier transforms of photographs of OH* chemiluminescence. A light-duty optical diesel engine has been used to perform the OH* bandpass imaging on. Four experimental points are evaluated, with injection timings in the homogeneous regime as well as in the stratified partially premixed combustion regime. Two-dimensional Fourier transforms translate these chemiluminescence images into a range of spatial frequencies. The frequency information is used to define combustion stratification, using a novel normalization procedure. The results indicate that this new definition, based on Fourier analysis of OH* bandpass images, overcomes the drawbacks of previous definitions used in the literature and is a promising method to compare the level of combustion stratification between different experiments.

Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain

A fusion algorithm based on target extraction for infrared image (IIR) and visible image fusion in the nonsubsampled contourlet transform (NSCT) domain is proposed. Commonly, the target information in IIR is important; in order to fully retain the target information in a final fused image, first, use maximum between-class variance method to segment IIR, such that the target regions with salient objects are extracted to produce the background and target images. Next, the visible and background images are decomposed to a series of low-pass and band-pass images by NSCT, respectively. Then, fuse the obtained images to produce the fused background image by different strategies in each band, in which Gaussian fuzzy logic is used to produce the low-pass coefficient; the spatial frequency of each band-pass image is used to determine the linking strength β value of pulse coupled neural network structure, and the result is used to fuse the band-pass images. Eventually, the fused image is produced combining the target image and the fused background image. The experiments show that this algorithm can retain more background details of the two images and highlight the target in the infrared image more effectively, as well as obviously improve the visual effect of the fusion image.

Satellite Image Resolution Enhancement Using Nonsubsampled Contourlet Transform and Clustering on Subbands

In this paper two new schemes for resolution enhancement (RE) of satellite images are proposed based on Nonsubsampled Contourlet Transform (NSCT). First one is based on the interpolation on band pass images obtained by applying NSCT on the input low resolution image. Similar to Demirel and Anbarjafari (IEEE Trans Geosci Remote Sens 49(6):1997–2004, 2011), as an intermediate step, the difference between approximation band and the input low resolution image is added with all the band pass directional subbands, to obtain a sharper image. This method is simple and computationally efficient but lacks sharp recovery of the edges due to the interpolation of band pass images. To overcome this, another method is proposed to obtain the difference layer, where dictionary is built using patches which are extracted from high resolution training image subbands. Similar patches from the dictionary are then clustered together. This method gives a much sharper image than the first method. Subjective and objective analysis of proposed methods reveals the superiority of the methods over conventional and other state-of-the-art RE methods.

Two-color spatial and temporal temperature measurements using a streaked soft x-ray imager.

A dual-channel streaked soft x-ray imager has been designed and used on high energy-density physics experiments at the National Ignition Facility. This streaked imager creates two images of the same x-ray source using two slit apertures and a single shallow angle reflection from a nickel mirror. Thin filters are used to create narrow band pass images at 510 eV and 360 eV. When measuring a Planckian spectrum, the brightness ratio of the two images can be translated into a color-temperature, provided that the spectral sensitivity of the two images is well known. To reduce uncertainty and remove spectral features in the streak camera photocathode from this photon energy range, a thin 100 nm CsI on 50 nm Al streak camera photocathode was implemented. Provided that the spectral shape is well-known, then uncertainties on the spectral sensitivity limits the accuracy of the temperature measurement to approximately 4.5% at 100 eV.

특이값 분해와 영상 피라미드를 이용한 대비 향상 알고리듬

This paper presents a novel contrast enhancement method based on singular value decomposition and image pyramid. The proposed method consists mainly of four steps. The proposed algorithm firstly decomposes image into band-pass images, including basis image and detail images, to improve both the global contrast and the local detail. In the global contrast process, singular value decomposition is used for contrast enhancement; the local detail scheme uses weighting factors. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. Experimental results show that the proposed algorithm improves contrast performance and enhances detail compared to conventional methods.

Testing MOND gravity in the shell galaxy NGC 3923

Context. The elliptical galaxy NGC 3923 is surrounded by numerous stellar shells that are concentric arcs centered on the galactic core. They are very likely a result of a minor merger and they consist of stars in nearly radial orbits. For a given potential, the shell radii at a given time after the merger can be calculated and compared to observations. The Modified Newtonian Dynamics (MOND) is a theory that aims to solve the missing mass problem by modifying the laws of classical dynamics in the limit of small accelerations. Hernquist & Quinn(1987) claimed that the shell distribution of NGC 3923 contradicted MOND, but Milgrom(1988) found several substantial insufficiencies in their work. Aims. We test whether the observed shell distribution in NGC 3923 is consistent with MOND using the current observational knowledge of the shell number and positions and of the host galaxy surface brightness profile, which supersede the data available in the 1980s when the last (and negative) tests of MOND viability were performed on NGC 3923. Methods. Using the 3.6 um bandpass image of NGC 3923 from the Spitzer space telescope we construct the mass profile of the galaxy. The evolution of shell radii in MOND is then computed using analytical formulae. We use 27 currently observed shells and allow for their multi-generation formation, unlike the Hernquist & Quinn one-generation model that used the 18 shells known at the time. Results. Our model reproduces the observed shell radii with a maximum deviation of 5% for 25 out of 27 known shells while keeping a reasonable formation scenario. A multi-generation nature of the shell system, resulting from successive passages of the surviving core of the tidally disrupted dwarf galaxy, is one of key ingredients of our scenario supported by the extreme shell radial range. The 25 reproduced shells are interpreted as belonging to three generations.

Improved image enhancement method for flotation froth image based on parameter extraction

Froth image could strongly indicate the production status in mineral flotation process. Considering low contrast and sensitivity to noises and illumination of froth images in flotation cells, an improved image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) and multiscale Retinex algorithm has been proposed. Nonsubsampled contourlet transform was firstly adopted to decompose the flotation froth images, ensure signals invariance and avoid the blurring edge. Secondly, a multiscale Retinex algorithm was used to enhance the lower frequency image and improve the brightness uniformity. Adaptive classification method based on Bayes atrophy threshold was proposed to eliminate noise, preserve strong edges, and enhance weak edges of band-pass sub-band images. Experiment shows that the proposed method could enhance the edge, contour, details and curb noise, and improve visual effects. Under-segmentation caused by noise and blurring edge has been solved, which lays a foundation for extracting foamy morphological flotation froth and analyzing grade.

A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex

Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.

Surpassing digital holography limits by lensless object scanning holography

We present lensless object scanning holography (LOSH) as a fully lensless method, capable of improving image quality in reflective digital Fourier holography, by means of an extremely simplified experimental setup. LOSH is based on the recording and digital post-processing of a set of digital lensless holograms and results in a synthetic image with improved resolution, field of view (FOV), signal-to-noise ratio (SNR), and depth of field (DOF). The superresolution (SR) effect arises from the generation of a synthetic aperture (SA) based on the linear movement of the inspected object. The same scanning principle enlarges the object FOV. SNR enhancement is achieved by speckle suppression and coherent artifacts averaging due to the coherent addition of the multiple partially overlapping bandpass images. And DOF extension is performed by digital refocusing to different object's sections. Experimental results showing an impressive image quality improvement are reported for a one-dimensional reflective resolution test target.

Resolution improvement by single-exposure superresolved interferometric microscopy with a monochrome sensor

Single-exposure superresolved interferometric microscopy (SESRIM) by RGB multiplexing has recently been proposed as a way to achieve one-dimensional superresolved imaging in digital holographic microscopy by a single-color CCD snapshot [Opt. Lett. 36, 885 (2011)]. Here we provide the mathematical basis for the operating principle of SESRIM, while we also present a different experimental configuration where the color CCD camera is replaced by a monochrome (B&W) CCD camera. To maintain the single-exposure working principle, the object field of view (FOV) is restricted and the holographic recording is based on image-plane wavelength-dispersion spatial multiplexing to separately record the three bandpass images. Moreover, a two-dimensional extension is presented by considering two options: time multiplexing and selective angular multiplexing. And as an additional implementation, the FOV restriction is eliminated by varying the angle between the three reference beams in the interferometric recording. Experimental results are reported for all of the above-mentioned cases.

Single-exposure two-dimensional superresolution in digital holography using a vertical cavity surface-emitting laser source array

We present a new implementation capable of producing two-dimensional (2D) superresolution (SR) imaging in a single exposure by aperture synthesis in digital lensless Fourier holography when using angular multiplexing provided by a vertical cavity surface-emitting laser source array. The system performs the recording in a single CCD snapshot of a multiplexed hologram coming from the incoherent addition of multiple subholograms, where each contains information about a different 2D spatial frequency band of the object's spectrum. Thus, a set of nonoverlapping bandpass images of the input object can be recovered by Fourier transformation (FT) of the multiplexed hologram. The SR is obtained by coherent addition of the information contained in each bandpass image while generating an enlarged synthetic aperture. Experimental results demonstrate improvement in resolution and image quality.

Heterogeneity of Focal Breast Lesions and Surrounding Tissue Assessed by Mammographic Texture Analysis: Preliminary Evidence of an Association with Tumor Invasion and Estrogen Receptor Status

Aim: This pilot study investigates whether heterogeneity in focal breast lesions and surrounding tissue assessed on mammography is potentially related to cancer invasion and hormone receptor status. Materials and Methods: Texture analysis (TA) assessed the heterogeneity of focal lesions and their surrounding tissues in digitized mammograms from 11 patients randomly selected from an imaging archive [ductal carcinoma in situ (DCIS) only, n = 4; invasive carcinoma (IC) with DCIS, n = 3; IC only, n = 4]. TA utilized band-pass image filtration to highlight image features at different spatial frequencies (filter values: 1.0–2.5) from fine to coarse texture. The distribution of features in the derived images was quantified using uniformity. Results: Significant differences in uniformity were observed between patient groups for all filter values. With medium scale filtration (filter value = 1.5) pure DCIS was more uniform (median = 0.281) than either DCIS with IC (median = 0.246, p = 0.0102) or IC (median = 0.249, p = 0.0021). Lesions with high levels of estrogen receptor expression were more uniform, most notably with coarse filtration (filter values 2.0 and 2.5, rs = 0.812, p = 0.002). Comparison of uniformity values in focal lesions and surrounding tissue showed significant differences between DCIS with or without IC versus IC (p = 0.0009). Conclusion: This pilot study shows the potential for computer-based assessments of heterogeneity within focal mammographic lesions and surrounding tissue to identify adverse pathological features in mammographic lesions. The technique warrants further investigation as a possible adjunct to existing computer aided diagnosis systems.

Image enhancement using a fusion framework of histogram equalization and laplacian pyramid

The image enhancement methods based on histogram equalization (HE) often fail to improve local information and sometimes have the fatal flaw of over-enhancement when a quantum jump occurs in the cumulative distribution function of the histogram. To overcome these shortcomings, we propose an image enhancement method based on a modified Laplacian pyramid framework that decomposes an image into band-pass images to improve both the global contrast and local information. For the global contrast, a novel robust HE is proposed to provide a well-balanced mapping function which effectively suppresses the quantum jump. For the local information, noise-reduced and adaptively gained high-pass images are applied to the resultant image. In qualitative and quantitative comparisons through experimental results, the proposed method shows natural and robust image quality and suitability for video sequences, achieving generally higher performance when compared to existing methods.