Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Restoration of Atmospheric Turbulence Degraded Video using Kurtosis Minimization and Motion Compensation

In this thesis work, the background of atmospheric turbulence degradation in imaging was reviewed and two aspects are highlighted: blurring and geometric distortion. The turbulence burring parameter is determined by the atmospheric turbulence condition that is often unknown; therefore, a blur identification technique was developed that is based on a higher order statistics (HOS). It was observed that the kurtosis generally increases as an image becomes blurred (smoothed). Such an observation was interpreted in the frequency domain in terms of phase correlation. Kurtosis minimization based blur identification is built upon this observation. It was shown that kurtosis minimization is effective in identifying the blurring parameter directly from the degraded image. Kurtosis minimization is a general method for blur identification. It has been tested on a variety of blurs such as Gaussian blur, out of focus blur as well as motion blur. To compensate for the geometric distortion, earlier work on the turbulent motion compensation was extended to deal with situations in which there is camera/object motion. Trajectory smoothing is used to suppress the turbulent motion while preserving the real motion. Though the scintillation effect of atmospheric turbulence is not considered separately, it can be handled the same way as multiple frame denoising while motion trajectories are built.Ph.D.Committee Chair: Mersereau, Russell; Committee Co-Chair: Smith, Mark; Committee Member: Lanterman, Aaron; Committee Member: Wang, May; Committee Member: Tannenbaum, Allen; Committee Member: Williams, Dougla

Line-Field Based Adaptive Image Model for Blind Deblurring

Ph.DDOCTOR OF PHILOSOPH

Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L

Development of bioinformatics tools to track cancer cell invasion using 3D in vitro invasion assays

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Frequency domain subpixel registration using HOG phase correlation

We present a novel frequency-domain image registration technique, which employs histograms of oriented gradients providing subpixel estimates. Our method involves image filtering using dense Histogram of Oriented Gradients (HOG), which provides an advanced representation of the images coping with real-world registration problems such as non-overlapping regions and small deformations. The proposed representation retains the orientation information and the corresponding weights in a multi-dimensional representation. Furthermore, due to the overlapping local contrast normalization characteristic of HOG, the proposed Histogram of Oriented Gradients - Phase Correlation (HOG-PC) method improves significantly the estimated motion parameters in small size blocks. Experiments using sequences with and without ground truth including both global and local/multiple motions demonstrate that the proposed method out- performs the state-of-the-art in frequency-domain motion estimation, in the shape of phase correlation, in terms of subpixel accuracy and motion compensation prediction for a range of test material, block sizes and motion scenarios