Researchers at the University of California, Davis have developed an optical lens module that uses a metalens or a metalens array having a controllable angular field-of-view.

Researchers at the University of California, Davis have developed two designs capable of capturing hyperspectral images that can be processed using compressive sensing techniques. These advanced component technologies for hyper-spectral imagers realizing 100x reduced size, weight, and power while supporting 1000x framerates in support of high performance.

Researchers at the University of California, Davis have developed a Doppler radar front end to overcome detection nulls without quadrature demodulation.

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An innovative technology that uses a device to move any imaging device precisely through a path in 3D space, enabling the generation of high-resolution 3D models.

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Volumetric additive manufacturing and vat-polymerization 3D printing methods rapidly solidify freeform objects via photopolymerization, but problematically raises the local temperature in addition to degree-of-conversion (DOC). The generated heat can critically affect the printing process as it can auto-accelerate the polymerization reaction, trigger convection flows, and cause optical aberrations. Therefore, temperature measurement alongside conversion state monitoring is crucial for devising mitigation strategies and implementing process control. Traditional infrared imaging suffers from multiple drawbacks such as limited transmission of measurement signal, material-dependent absorptions, and high background signals emitted by other objects. Consequently, a viable temperature and DOC monitoring method for volumetric 3D printing doesn’t exist.To address this opportunity, UC Berkeley researchers have developed a tomographic imaging technique that detects the spatiotemporal evolution of temperature and DOC during volumetric printing. The invention lays foundations for the development of volumetric measurement systems that uniquely resolve both temperature and DOC in volumetric printing.This novel Berkeley measurement system is envisaged as an integral tool for existing manufacturing technologies, such as computed axial lithography (CAL, Tech ID #28754), and as a new research tool for commercial biomanufacturing, general fluid dynamics, and more.

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

Novel system for imaging of specimens in high refractive index solutions on the Zeiss Z.1 fluorescence light sheet microscope. System will allow for deep imaging of large and intact biological samples (i.e. mouse brain) for enhanced optical resolution and faster imaging speed.

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a high operating burden, especially as the number of pixels increases, and incomplete coverage of the optical surface. This results in high device complexity, and cost, as well as enormous real-time computation requirements, reduced optical performance, and optical artifacts.To address these problems, researchers at UC Berkeley have developed a method for wiring groups of pixels, such as annular rings, parallel strips, or radial strips. This takes advantage of the fact that most spatial light modulation tasks can be accomplished by combining a number of simple “primitive phase profiles”, in which not all pixels need be independent of each other. In this co-wiring method, individual optical elements remain at the pixel level, but are wired together in a way that they move in precisely the coordinated manner to produce one of these primitive phase profiles. This allows for high frame rates, high coverage of the optical plane, and a degree of sensitivity impossible to produce with large, geometric optical elements that exist in prior art.

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a fundamental restriction on frame rate which has led manufacturers to seek the diminishing returns of continually increasing pixel number, resulting in impractical device sizes, complexity, and cost, as well as enormous real-time computation requirements. Additionally, these devices inherently produce monochromatic and speckled frames due to the requirement that the input beam be coherent.To address these problems, researchers at UC Berkeley have developed a device which can perform spatial light modulation with a frame rate ~20 times higher than existing technologies. This allows for a smaller number of pixels to produce high resolution, full color images by interleaving images of different colors and scanning rapidly across a screen in a similar way to the operation of CRT televisions Researchers have also developed an efficient and robust fabrication method, which combined with the smaller pixel number of these devices could cause them to be much more cost effective than existing technologies.

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Researchers at the University of California, Davis have developed a system consisting of cameras and multi-wavelength lasers that is capable of precisely locating and inspecting items.

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Researchers at UCI have created an elastography technique, which combines X-ray computed tomography (CT) and sound wave integration.  This adapted elastographic technique avoids the issues faced by ultrasound alone and permits medical imaging of deep tissue and measures the mechanical properties of materials.

Many areas of intellectual property law involve subjective judgments regarding confusion or similarity. For example, in trademark or trade dress lawsuits a key factor considered by the court is the degree of visual similarity between the trademark or product designs under consideration. Such similarity judgments are nontrivial, and may be complicated by cognitive factors such as categorization, memory, and reasoning that vary substantially across individuals. Currently, three forms of evidence are widely accepted: visual comparison by litigants, expert witness testimonies, and consumer surveys. All three rely on subjective reports of human responders, whether litigants, expert witnesses, or consumer panels. Consequently, all three forms of evidence potentially share the criticism that they are subject to overt (e.g. conflict of interest) or covert (e.g. inaccuracy of self-report) biases.To address this situation, researchers at UC Berkeley developed a technology that directly measures the mental state of consumers when they attend to visual images of consumer products, without the need for self-report measures such as questionnaires or interviews. In so doing, this approach reduces the potential for biased reporting.  

The disclosure relates to method of processing three-dimensional images or volumetric datasets to determine a configuration of a medium or a rate of a change of the medium, wherein the method includes tracking changes of a field related to the medium to obtain a deformation or velocity field in three dimensions. In some cases, the field is a brightness field inherent to the medium or its motion. In other embodiments, the brightness field is from a tracking agent that includes floating particles detectable in the medium during flow of the medium.  

UCLA researchers in the Department of Electrical Engineering have developed a novel deep learning-based algorithm that digitally reconstructs images from holography over an extended depth of field.

Researchers at UCI have developed an image processing platform capable of visualizing 3D blood flow dynamics of the heart in real-time. This technology aims to be a promising tool for looking at areas of the heart that were previously difficult to image and to better understand the dynamics in cardiac dysfunctions.

UCLA researchers in the Department of Electrical Engineering have developed an on-chip UV holographic imaging microscope that offers a low-cost, portable, and robust technique to image and distinguish protein crystals from salt crystals.

UCLA researchers in the Department of Electrical Engineering have developed a novel deep neural network that generates speckle- and artifact-free high-quality images at different sample depths from a single hologram.  The resulting images are equivalent to bright-field images taken throughout a 3D sample.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a novel single-objective lens stereo imaging setup for endoscopic applications.

UCLA researchers have developed an algorithm that enables construction of 3D images from tomographic data through iterative methods with the incorporation of mathematical constraints. This methodology is an improvement over conventional techniques as it allows for radiation dose reduction and improved resolution.

Researchers from the Department of Radiation Oncology at UCLA have developed a novel method that enables 3D patient monitoring during radiation therapy that enables remote patient visualization with high spatial resolution.

Researchers in the UCLA Department of Radiation Oncology have developed a novel means to remotely visualize a radiotherapy treatment room in real-time via 3D camera technology.