85 research outputs found

    High-Fidelity and Perfect Reconstruction Techniques for Synthesizing Modulation Domain Filtered Images

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    Biomimetic processing inspired by biological vision systems has long been a goal of the image processing research community, both to further understanding of what it means to perceive and interpret image content and to facilitate advancements in applications ranging from processing large volumes of image data to engineering artificial intelligence systems. In recent years, the AM-FM transform has emerged as a useful tool that enables processing that is intuitive to human observers but would be difficult or impossible to achieve using traditional linear processing methods. The transform makes use of the multicomponent AM-FM image model, which represents imagery in terms of amplitude modulations, representative of local image contrast, and frequency modulations, representative of local spacing and orientation of lines and patterns. The model defines image components using an array of narrowband filterbank channels that is designed to be similar to the spatial frequency channel decomposition that occurs in the human visual system. The AM-FM transform entails the computation of modulation functions for all components of an image and the subsequent exact recovery of the image from those modulation functions. The process of modifying the modulation functions to alter visual information in a predictable way and then recovering the modified image through the AM-FM transform is known as modulation domain filtering. Past work in modulation domain filtering has produced dramatic results, but has faced challenges due to phase wrapping inherent in the transform computations and due to unknown integration constants associated with modified frequency content. The approaches developed to overcome these challenges have led to a loss of both stability and intuitive simplicity within the AM-FM model. In this dissertation, I have made significant advancements in the underlying processes that comprise the AM-FM transform. I have developed a new phase unwrapping method that increases the stability of the AM-FM transform, allowing higher quality modulation domain filtering results. I have designed new reconstruction techniques that allow for successful recovery from modified frequency modulations. These developments have allowed the design of modulation domain filters that, for the first time, do not require any departure from the simple and intuitive nature of the basic AM-FM model. Using the new modulation domain filters, I have produced new and striking results that achieve a variety of image processing tasks which are motivated by biological visual perception. These results represent a significant advancement relative to the state of the art and are a foundation from which future advancements in the field may be attained

    AM-FM methods for image and video processing

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    This dissertation is focused on the development of robust and efficient Amplitude-Modulation Frequency-Modulation (AM-FM) demodulation methods for image and video processing (there is currently a patent pending that covers the AM-FM methods and applications described in this dissertation). The motivation for this research lies in the wide number of image and video processing applications that can significantly benefit from this research. A number of potential applications are developed in the dissertation. First, a new, robust and efficient formulation for the instantaneous frequency (IF) estimation: a variable spacing, local quadratic phase method (VS-LQP) is presented. VS-LQP produces much more accurate results than current AM-FM methods. At significant noise levels (SNR \u3c 30dB), for single component images, the VS-LQP method produces better IF estimation results than methods using a multi-scale filterbank. At low noise levels (SNR \u3e 50dB), VS-LQP performs better when used in combination with a multi-scale filterbank. In all cases, VS-LQP outperforms the Quasi-Eigen Approximation algorithm by significant amounts (up to 20dB). New least squares reconstructions using AM-FM components from the input signal (image or video) are also presented. Three different reconstruction approaches are developed: (i) using AM-FM harmonics, (ii) using AM-FM components extracted from different scales and (iii) using AM-FM harmonics with the output of a low-pass filter. The image reconstruction methods provide perceptually lossless results with image quality index values bigger than 0.7 on average. The video reconstructions produced image quality index values, frame by frame, up to more than 0.7 using AM-FM components extracted from different scales. An application of the AM-FM method to retinal image analysis is also shown. This approach uses the instantaneous frequency magnitude and the instantaneous amplitude (IA) information to provide image features. The new AM-FM approach produced ROC area of 0.984 in classifying Risk 0 versus Risk 1, 0.95 in classifying Risk 0 versus Risk 2, 0.973 in classifying Risk 0 versus Risk 3 and 0.95 in classifying Risk 0 versus all images with any sign of Diabetic Retinopathy. An extension of the 2D AM-FM demodulation methods to three dimensions is also presented. New AM-FM methods for motion estimation are developed. The new motion estimation method provides three motion estimation equations per channel filter (AM, IF motion equations and a continuity equation). Applications of the method in motion tracking, trajectory estimation and for continuous-scale video searching are demonstrated. For each application, we discuss the advantages of the AM-FM methods over current approaches

    Analyzing Image Structure by Multidimensional Frequency Modulation

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    Multirate Frequency Transformations: Wideband AM-FM Demodulation with Applications to Signal Processing and Communications

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    The AM-FM (amplitude & frequency modulation) signal model finds numerous applications in image processing, communications, and speech processing. The traditional approaches towards demodulation of signals in this category are the analytic signal approach, frequency tracking, or the energy operator approach. These approaches however, assume that the amplitude and frequency components are slowly time-varying, e.g., narrowband and incur significant demodulation error in the wideband scenarios. In this thesis, we extend a two-stage approach towards wideband AM-FM demodulation that combines multirate frequency transformations (MFT) enacted through a combination of multirate systems with traditional demodulation techniques, e.g., the Teager-Kasiser energy operator demodulation (ESA) approach to large wideband to narrowband conversion factors. The MFT module comprises of multirate interpolation and heterodyning and converts the wideband AM-FM signal into a narrowband signal, while the demodulation module such as ESA demodulates the narrowband signal into constituent amplitude and frequency components that are then transformed back to yield estimates for the wideband signal. This MFT-ESA approach is then applied to the various problems of: (a) wideband image demodulation and fingerprint demodulation, where multidimensional energy separation is employed, (b) wideband first-formant demodulation in vowels, and (c) wideband CPM demodulation with partial response signaling, to demonstrate its validity in both monocomponent and multicomponent scenarios as an effective multicomponent AM-FM signal demodulation and analysis technique for image processing, speech processing, and communications based applications

    ANALYZING THE EFFECT OF POLARIZATION IN IMAGING

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    Light as the natural element for our life can be characterized by its intensity, wavelength and polarization. The polarization is general characteristic of wave (light, gravitational wave, sound wave etc.) that have the information of their oscillations as well as the reflecting object. Polarization of light could not be viewed naturally by our naked human eyes due to the limitation of capabilities of capturing light on a muscle known as the ciliary muscle. Nowadays, in the computer vision, the polarization is used to determine image segmentation, object and texture recognition. Moreover, in the medical field, polarization is used to allow better the diagnose of skin texture and lesion. This project uses digital image processing technique to analyze the effect of polarization in imaging, which focuses on identifying the textures or patterns of an object. In the polarization on human skin’s imaging, this analysis technique is developed to classify and determine the texture of human skin based on the different races background with the aid of polarized light as well to distinguish between the texture of normal skin and skin lesion

    Modulation Domain Image Processing

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    The classical Fourier transform is the cornerstone of traditional linearsignal and image processing. The discrete Fourier transform (DFT) and thefast Fourier transform (FFT) in particular led toprofound changes during the later decades of the last century in howwe analyze and process 1D and multi-dimensional signals.The Fourier transform represents a signal as an infinite superpositionof stationary sinusoids each of which has constant amplitude and constantfrequency. However, many important practical signals such as radar returnsand seismic waves are inherently nonstationary. Hence, more complextechniques such as the windowed Fourier transform and the wavelet transformwere invented to better capture nonstationary properties of these signals.In this dissertation, I studied an alternative nonstationary representationfor images, the 2D AM-FM model. In contrast to thestationary nature of the classical Fourier representation, the AM-FM modelrepresents an image as a finite sum of smoothly varying amplitudesand smoothly varying frequencies. The model has been applied successfullyin image processing applications such as image segmentation, texture analysis,and target tracking. However, these applications are limitedto \emph{analysis}, meaning that the computed AM and FM functionsare used as features for signal processing tasks such as classificationand recognition. For synthesis applications, few attempts have been madeto synthesize the original image from the AM and FM components. Nevertheless,these attempts were unstable and the synthesized results contained artifacts.The main reason is that the perfect reconstruction AM-FM image model waseither unavailable or unstable. Here, I constructed the first functionalperfect reconstruction AM-FM image transform that paves the way for AM-FMimage synthesis applications. The transform enables intuitive nonlinearimage filter designs in the modulation domain. I showed that these filtersprovide important advantages relative to traditional linear translation invariant filters.This dissertation addresses image processing operations in the nonlinearnonstationary modulation domain. In the modulation domain, an image is modeledas a sum of nonstationary amplitude modulation (AM) functions andnonstationary frequency modulation (FM) functions. I developeda theoretical framework for high fidelity signal and image modeling in themodulation domain, constructed an invertible multi-dimensional AM-FMtransform (xAMFM), and investigated practical signal processing applicationsof the transform. After developing the xAMFM, I investigated new imageprocessing operations that apply directly to the transformed AM and FMfunctions in the modulation domain. In addition, I introduced twoclasses of modulation domain image filters. These filters produceperceptually motivated signal processing results that are difficult orimpossible to obtain with traditional linear processing or spatial domainnonlinear approaches. Finally, I proposed three extensions of the AM-FMtransform and applied them in image analysis applications.The main original contributions of this dissertation include the following.- I proposed a perfect reconstruction FM algorithm. I used aleast-squares approach to recover the phase signal from itsgradient. In order to allow perfect reconstruction of the phase function, Ienforced an initial condition on the reconstructed phase. The perfectreconstruction FM algorithm plays a critical role in theoverall AM-FM transform.- I constructed a perfect reconstruction multi-dimensional filterbankby modifying the classical steerable pyramid. This modified filterbankensures a true multi-scale multi-orientation signal decomposition. Such adecomposition is required for a perceptually meaningful AM-FM imagerepresentation.- I rotated the partial Hilbert transform to alleviate ripplingartifacts in the computed AM and FM functions. This adjustment results inartifact free filtering results in the modulation domain.- I proposed the modulation domain image filtering framework. Iconstructed two classes of modulation domain filters. I showed that themodulation domain filters outperform traditional linear shiftinvariant (LSI) filters qualitatively and quantitatively in applicationssuch as selective orientation filtering, selective frequency filtering,and fundamental geometric image transformations.- I provided extensions of the AM-FM transform for image decompositionproblems. I illustrated that the AM-FM approach can successfullydecompose an image into coherent components such as textureand structural components.- I investigated the relationship between the two prominentAM-FM computational models, namely the partial Hilbert transformapproach (pHT) and the monogenic signal. The established relationshiphelps unify these two AM-FM algorithms.This dissertation lays a theoretical foundation for future nonlinearmodulation domain image processing applications. For the first time, onecan apply modulation domain filters to images to obtain predictableresults. The design of modulation domain filters is intuitive and simple,yet these filters produce superior results compared to those of pixeldomain LSI filters. Moreover, this dissertation opens up other research problems.For instance, classical image applications such as image segmentation andedge detection can be re-formulated in the modulation domain setting.Modulation domain based perceptual image and video quality assessment andimage compression are important future application areas for the fundamentalrepresentation results developed in this dissertation

    ANALYZING THE EFFECT OF POLARIZATION IN IMAGING

    Get PDF
    Light as the natural element for our life can be characterized by its intensity, wavelength and polarization. The polarization is general characteristic of wave (light, gravitational wave, sound wave etc.) that have the information of their oscillations as well as the reflecting object. Polarization of light could not be viewed naturally by our naked human eyes due to the limitation of capabilities of capturing light on a muscle known as the ciliary muscle. Nowadays, in the computer vision, the polarization is used to determine image segmentation, object and texture recognition. Moreover, in the medical field, polarization is used to allow better the diagnose of skin texture and lesion. This project uses digital image processing technique to analyze the effect of polarization in imaging, which focuses on identifying the textures or patterns of an object. In the polarization on human skin’s imaging, this analysis technique is developed to classify and determine the texture of human skin based on the different races background with the aid of polarized light as well to distinguish between the texture of normal skin and skin lesion
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