A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Systems and methods for implementing array cameras configured to perform super- resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. Lens stack arrays in accordance with many embodiments of the invention include lens elements formed on substrates separated by spacers, where the lens elements, substrates and spacers are configured to form a plurality of optical channels, at least one aperture located within each optical channel, at least one spectral filter located within each optical channel, where each spectral filter is configured to pass a specific spectral band of light, and light blocking materials located within the lens stack array to optically isolate the optical channels.

Capturing and processing of images using monolithic camera array with heterogeneous imagers

From Cascaded Catalytic Nucleic Acids to Enzyme–DNA Nanostructures: Controlling Reactivity, Sensing, Logic Operations, and Assembly of Complex Structures

Deep learning has been proven to yield reliably generalizable solutions to numerous classification and decision tasks. Here, we demonstrate for the first time to our knowledge that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging. We experimentally built and tested a lensless imaging system where a DNN was trained to recover phase objects given their propagated intensity diffraction patterns.

Lensless computational imaging through deep learning

We present a machine-learning-based method for single-shot imaging through scattering media. The inverse scattering process was calculated based on a nonlinear regression algorithm by learning a number of training object-speckle pairs. In the experimental demonstration, multilayer phase objects between scattering plates were reconstructed from intensity measurements. Our approach enables model-free sensing, where it is not necessary to know the sensing processes/models.

Learning-based imaging through scattering media

We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources.

Deep learning wavefront sensing

We present a method for computer-generated holography based on deep learning. The inverse process of light propagation is regressed with a number of computationally generated speckle data sets. This method enables noniterative calculation of computer-generated holograms (CGHs). The proposed method was experimentally verified with a phase-only CGH.

Deep-learning-generated holography

Typical compound-eye imaging systems consist of multiple lenslets arranged regularly because of the advantages of these arrangements, such as simplicity in design, fabrication, and data processing Such a regular arrangement, however, exhibits strong fluctuation of the imaging performance over the object distance To solve this problem, irregularity is introduced, and this approach is applied to a compound-eye imaging system called thin observation module by bound optics (TOMBO) An efficient design method for improving the imaging performance is presented Simulation results, including the peak signal-to-noise ratio of the super-resolved images, confirm the effectiveness of the proposed method

https://iopscience.iop.org/article/10.1143/APEX.3.022501/pdf

Irregular Lens Arrangement Design to Improve Imaging Performance of Compound-Eye Imaging Systems

A three-dimensional object imaging device comprises a compound-eye imaging unit and an image reconstructing unit for reconstructing an image of a three-dimensional object based on multiple unit images captured by the imaging unit. Based on the unit images obtained by the imaging unit, the image reconstructing unit calculates a distance (hereafter “pixel distance”) between the object and the imaging unit for each pixel forming the unit images, and rearranges the unit images pixel-by-pixel on a plane at the pixel distance to create a reconstructed image. Preferably, the image reconstructing unit sums a high-frequency component reconstructed image created from the multiple unit images with a lower noise low-frequency component unit image selected from low-frequency component unit images created from the multiple unit images so as to form a reconstructed image of the three-dimensional object. This makes it possible to obtain a reconstructed image with high definition easily by a simple process.

Jun Tanida

Papers

Learning-based imaging through scattering media

Deep learning wavefront sensing

Deep-learning-generated holography

Irregular Lens Arrangement Design to Improve Imaging Performance of Compound-Eye Imaging Systems

Three-Dimensional Object Imaging Device