Image science investigates the ways that image quality can be defined, measured and optimized; it touches and improves the visualization of everything from healthy bones to unstable atmospheres to millennia-old geological formations. This interdisciplinary field studies the physics of photon generation, the propagation of light through optical systems, signal generation in detectors and more, and considers the statistics of random processes and how they affect the information contained within images.
The faculty in image science at the Wyant College of Optical Sciences show particular strength in designing new technology for medical imaging, homeland security, earth sciences and other applications, and in developing new methods for assessing image quality by quantifying how accurately imaging systems can accomplish certain analytical tasks.
1) Next-gen imaging takes pictures that speak a million pixels: Growth of computational imaging could drive it beyond the limits of optical systems
Professor David Brady's work in computational imaging attracted the attention of writers at SPIE who focused an article on the work he is doing to build a camera capable of creating the world's first gigapixel images. The machine contains an array of 98 microcameras with microprocessors that can stitch the individual images together. According to the article, "Computational imaging, on the other hand, allows users to refocus a photo, construct a 3D picture, combine wavelengths, or stitch together separate images into one.
2) Snapshot ptychography on array cameras
A new paper in Optics Express Vol. 30, Issue 2 features the work of Chengyu Wang, Minghao Hu, Yuzuru Takashima, Timothy J. Schulz, and David J. Brady. The team uses convolutional neural networks to recover images optically down-sampled by 6.7 × using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, here the researchers apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve signal-to-noise ratio (SNR).
3) Smartphone Confocal Microscopy for Imaging Cellular Structures in Human Skin in Vivo
Using a slip aperature and diffraction grating on a smartphone, the research team, including Dongkyun "DK" Kang, has developed a confocal microscope. It's use for in vivo human skin imaging was successful, conducting two-dimensional confocal imaging without the need for any beam scanning devices. These results suggest that the smartphone confocal microscope has a potential to be a low-cost option capable of examining cellular details in vivo and may help disease diagnosis in resource-poor settings, where conducting standard histopathological analysis is challenging.
4) Intelligent Imaging and Sensing Laboratory
As imaging devices — from smartphones to military drones to security cameras to cars — become ubiquitous, rising data volume and processing demands become problematic. Compression is routinely employed to reduce image file sizes for convenient storage and transmission. However, the success of image compression techniques suggests that traditional imaging systems can be highly inefficient, collecting redundant data that could be compressed without significant degradation. The field of compressive imaging addresses this shortcoming by acquiring a “compressed image” directly in the optical domain.
5) Center for Gamma Ray Imaging
Nuclear imaging modalities, such as positron emission tomography and single-photon emission computed tomography, form an important element of modern medical diagnostics. The University of Arizona Center for Gamma-Ray Imaging, led by Harrison H. Barrett in cooperation with Lars R. Furenlid, Matthew A. Kupinski and Eric W. Clarkson, focuses on advancing the state of the art in radionuclide imaging (e.g., PET and SPECT). The CGRI uniquely combines rigorous theory, inventive computational tools, advanced detectors and electronics, innovative imaging systems, novel radiotracers and cutting-edge clinical and preclinical applications. This work is done within the context of gamma-ray imaging, but it is important to other forms of medical imaging and image science in general.