Automatic Estimation of Modulation Transfer Functions

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available

Architectures for computational photography

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 93-94).Computational photography refers to a wide range of image capture and processing techniques that extend the capabilities of digital photography and allow users to take photographs that could not have been taken by a traditional camera. Since its inception less than a decade ago, the field today encompasses a wide range of techniques including high dynamic range (HDR) imaging, low light enhancement, panorama stitching, image deblurring and light field photography. These techniques have so far been software based, which leads to high energy consumption and typically no support for real-time processing. This work focuses on hardware architectures for two algorithms - (a) bilateral filtering which is commonly used in computational photography applications such as HDR imaging, low light enhancement and glare reduction and (b) image deblurring. In the first part of this work, digital circuits for three components of a multi-application bilateral filtering processor are implemented - the grid interpolation block, the HDR image creation and contrast adjustment blocks, and the shadow correction block. An on-chip implementation of the complete processor, designed with other team members, performs HDR imaging, low light enhancement and glare reduction. The 40 nm CMOS test chip operates from 98 MHz at 0.9 V to 25 MHz at 0.9 V and processes 13 megapixels/s while consuming 17.8 mW at 98 MHz and 0.9 V, achieving significant energy reduction compared to previous CPU/GPU implementations. In the second part of this work, a complete system architecture for blind image deblurring is proposed. Digital circuits for the component modules are implemented using Bluespec SystemVerilog and verified to be bit accurate with a reference software implementation. Techniques to reduce power and area cost are investigated and synthesis results in 40nm CMOS technology are presentedby Priyanka Raina.S.M

Computational Multispectral Endoscopy

Minimal Access Surgery (MAS) is increasingly regarded as the de-facto approach in interventional medicine for conducting many procedures this is due to the reduced patient trauma and consequently reduced recovery times, complications and costs. However, there are many challenges in MAS that come as a result of viewing the surgical site through an endoscope and interacting with tissue remotely via tools, such as lack of haptic feedback; limited field of view; and variation in imaging hardware. As such, it is important best utilise the imaging data available to provide a clinician with rich data corresponding to the surgical site. Measuring tissue haemoglobin concentrations can give vital information, such as perfusion assessment after transplantation; visualisation of the health of blood supply to organ; and to detect ischaemia. In the area of transplant and bypass procedures measurements of the tissue tissue perfusion/total haemoglobin (THb) and oxygen saturation (SO2) are used as indicators of organ viability, these measurements are often acquired at multiple discrete points across the tissue using with a specialist probe. To acquire measurements across the whole surface of an organ one can use a specialist camera to perform multispectral imaging (MSI), which optically acquires sequential spectrally band limited images of the same scene. This data can be processed to provide maps of the THb and SO2 variation across the tissue surface which could be useful for intra operative evaluation. When capturing MSI data, a trade off often has to be made between spectral sensitivity and capture speed. The work in thesis first explores post processing blurry MSI data from long exposure imaging devices. It is of interest to be able to use these MSI data because the large number of spectral bands that can be captured, the long capture times, however, limit the potential real time uses for clinicians. Recognising the importance to clinicians of real-time data, the main body of this thesis develops methods around estimating oxy- and deoxy-haemoglobin concentrations in tissue using only monocular and stereo RGB imaging data

Superresolution imaging: A survey of current techniques

Cristóbal, G., Gil, E., Šroubek, F., Flusser, J., Miravet, C., Rodríguez, F. B., “Superresolution imaging: A survey of current techniques”, Proceedings of SPIE - The International Society for Optical Engineering, 7074, 2008. Copyright 2008. Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.This research has been partially supported by the following grants: TEC2007-67025/TCM, TEC2006-28009-E, BFI-2003-07276, TIN-2004-04363-C03-03 by the Spanish Ministry of Science and Innovation, and by PROFIT projects FIT-070000-2003-475 and FIT-330100-2004-91. Also, this work has been partially supported by the Czech Ministry of Education under the project No. 1M0572 (Research Center DAR) and by the Czech Science Foundation under the project No. GACR 102/08/1593 and the CSIC-CAS bilateral project 2006CZ002

Robust Magnetic Resonance Imaging of Short T2 Tissues

Tissues with short transverse relaxation times are defined as ‘short T2 tissues’, and short T2 tissues often appear dark on images generated by conventional magnetic resonance imaging techniques. Common short T2 tissues include tendons, meniscus, and cortical bone. Ultrashort Echo Time (UTE) pulse sequences can provide morphologic contrasts and quantitative maps for short T2 tissues by reducing time-of-echo to the system minimum (e.g., less than 100 us). Therefore, UTE sequences have become a powerful imaging tool for visualizing and quantifying short T2 tissues in many applications. In this work, we developed a new Flexible Ultra Short time Echo (FUSE) pulse sequence employing a total of thirteen acquisition features with adjustable parameters, including optimized radiofrequency pulses, trajectories, choice of two or three dimensions, and multiple long-T2 suppression techniques. Together with the FUSE sequence, an improved analytical density correction and an auto-deblurring algorithm were incorporated as part of a novel reconstruction pipeline for reducing imaging artifacts. Firstly, we evaluated the FUSE sequence using a phantom containing short T2 components. The results demonstrated that differing UTE acquisition methods, improving the density correction functions and improving the deblurring algorithm could reduce the various artifacts, improve the overall signal, and enhance short T2 contrast. Secondly, we applied the FUSE sequence in bovine stifle joints (similar to the human knee) for morphologic imaging and quantitative assessment. The results showed that it was feasible to use the FUSE sequence to create morphologic images that isolate signals from the various knee joint tissues and carry out comprehensive quantitative assessments, using the meniscus as a model, including the mappings of longitudinal relaxation (T1) times, quantitative magnetization transfer parameters, and effective transverse relaxation (T2*) times. Lastly, we utilized the FUSE sequence to image the human skull for evaluating its feasibility in synthetic computed tomography (CT) generation and radiation treatment planning. The results demonstrated that the radiation treatment plans created using the FUSE-based synthetic CT and traditional CT data were able to present comparable dose calculations with the dose difference of mean less than a percent. In summary, this thesis clearly demonstrated the need for the FUSE sequence and its potential for robustly imaging short T2 tissues in various applications

PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addresse

Energy-efficient circuits and systems for computational imaging and vision on mobile devices

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 125-127).Eighty five percent of images today are taken by cell phones. These images are not merely projections of light from the scene onto the camera sensor but result from a deep calculation. This calculation involves a number of computational imaging algorithms such as high dynamic range (HDR) imaging, panorama stitching, image deblurring and low-light imaging that compensate for camera limitations, and a number of deep learning based vision algorithms such as face recognition, object recognition and scene understanding that make inference on these images for a variety of emerging applications. However, because of their high computational complexity, mobile CPU or GPU based implementations of these algorithms do not achieve real-time performance. Moreover, offloading these algorithms to the cloud is not a viable solution because wirelessly transmitting large amounts of image data results in long latency and high energy consumption, making them unsuitable for mobile devices. This work solves these problems by designing energy-efficient hardware accelerators targeted at these applications. It presents the architecture of two complete computational imaging systems for energy-constrained mobile environments: (1) an energy-scalable accelerator for blind image deblurring, with an on-chip implementation and (2) a low-power processor for real-time motion magnification in videos, with an FPGA implementation. It also presents a 3D imaging platform and image processing workflow for 3D surface area assessment of dermatologic lesions. It demonstrates that such accelerator-based systems can enable energy-efficient integration of computational imaging and vision algorithms into mobile and wearable devices.by Priyanka Raina.Ph. D