Comparing Multiple Turbulence Restoration Algorithms Performance on Noisy Anisoplanatic Imagery

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery

Impact of Detector-element Active-area Shape and Fill Factor on Image Sampling, Restoration, and Super-Resolution

In many undersampled imaging systems, spatial integration from the individual detector elements is the dominant component of the system point spread function (PSF). Conventional focal plane arrays (FPAs) utilize square detector elements with a nearly 100% fill factor, where fill factor is defined as the fraction of the detector element area that is active in light detection. A large fill factor is generally considered to be desirable because more photons are collected for a given pitch, and this leads to a higher signal-to-noise-ratio (SNR). However, the large active area works against super-resolution (SR) image restoration by acting as an additional low pass filter in the overall PSF when modeled on the SR sampling grid. A high fill factor also tends to increase blurring from pixel cross-talk. In this paper, we study the impact of FPA detector-element shape and fill factor on SR. A detailed modulation transfer function analysis is provided along with a number of experimental results with both simulated data and real data acquired with a midwave infrared (MWIR) imaging system. We demonstrate the potential advantage of low fill factor detector elements when combined with SR image restoration. Our results suggest that low fill factor circular detector elements may be the best choice. New video results are presented using robust adaptive Wiener filter SR processing applied to data from a commercial MWIR imaging system with both high and low detector element fill factors

Block Matching and Wiener Filtering Approach to Optical Turbulence Mitigation and Its Application to Simulated and Real Imagery with Quantitative Error Analysis

We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames are then averaged, and the average image is processed with a Wiener filter to provide deconvolution. An important aspect of the proposed method lies in how we model the degradation point spread function (PSF) for the purposes of Wiener filtering. We use a parametric model that takes into account the level of geometric correction achieved during image registration. This is unlike any method we are aware of in the literature. By matching the PSF to the level of registration in this way, the Wiener filter is able to fully exploit the reduced blurring achieved by registration. We also describe a method for estimating the atmospheric coherence diameter (or Fried parameter) from the estimated motion vectors. We provide a detailed performance analysis that illustrates how the key tuning parameters impact system performance. The proposed method is relatively simple computationally, yet it has excellent performance in comparison with state-of-the-art benchmark methods in our study

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment

Three-dimensional acoustic impedance map analysis of soft tissue

Three-dimensional impedance maps (3DZMs) are virtual volumes of acoustic impedance values constructed from histology to represent tissue microstructure acoustically. From the 3DZM, the ultrasonic backscattered power spectrum can be predicted and model based scatterer properties, such as effective scatterer diameter (ESD), can be estimated. Additionally, the 3DZM can be exploited to visualize and identify possible scattering sites, which may aid in the development of more effective scattering models to better represent the ultrasonic interaction with underlying tissue microstructure. The 3DZM construction and analysis algorithms have been improved both in terms of performance and cost. A multiresolution registration scheme has been implemented in order to robustly reconstruct the 3DZM from real tissue. Additional processing, such as photometric correction and automatic detection of damaged histology sections, has been applied to ensure the reconstructed volume is faithful to the properties of the original tissue. In this work, 3DZMs were created from a set of human fibroadenoma samples. ESD estimates were made assuming a fluid-filled sphere form factor model from 3DZMs of volume 300x300x300 cubic microns. For a collection of 33 independent human fibroadenoma tissue samples, the ESD was estimated to be 111.4 plus/minus 40.7 microns. The 3DZMs were then investigated visually to identify possible scattering sources which conformed to the estimated model scatterer dimensions. Additionally, 3DZMs were compared with quantitative ultrasound (QUS) techniques. A chemically fixed section of rabbit liver was scanned ultrasonically, then used to create 3DZMs. ESD estimates were made using the fluid-filled sphere form factor. Twenty-four 3DZMs of volume 300x300x300 cubic microns were constructed from the sample, and using two methods of ESD estimation, produced ESD estimates of 93.6 plus/minus 52.7 and 7.04 plus/minus 1.30 microns. ESD was also estimated from ultrasonic backscatter using transducers with center frequencies 7.5, 13, 20, 40, and 65 MHz. The ESD estimates from the backscatter data were 102.2 plus/minus 30.5, 63.3 plus/minus 12.2, 23.6 plus/minus 26.2, 19.8 plus/minus 1.1, and 2.78 plus/minus 6.43 microns respectively. This work provides new insights into the 3DZM technique, both in terms of its viability for tissue studies and its relationship to more traditional QUS techniques

Registration of Medical Images for the Construction of 3-D Impedance Maps

Recent developments in ultrasonic imaging techniques involve the display of quantitative information. Quantitative ultrasound (QUS) has the potential to become a reliable, fast, and inexpensive technique to classify (diagnose) pathologies. The understanding of QUS images relies extensively on digital signal processing strategies. This research attempts to use cutting edge image registration, interpolation, and other image processing techniques to build 3-D tissue models from a series of imperfect photomicrographs of three different types of tumors (rat fibro adenoma, mouse sarcoma, and mouse carcinoma). A 3-D impedance map can be constructed from these well-registered models. This computational model is an important tool for the correct understanding and interpretation of ultrasonic scattering in tissue, and ultimately, the development of QUS as a valuable diagnostic tool.unpublishednot peer reviewedU of I OnlyUndergraduate senior thesis not recommended for open acces