Two plus one is almost three: a fast approximation for multi-view deconvolution

Multi-view deconvolution is a powerful image-processing tool for light sheet fluorescence microscopy, providing isotropic resolution and enhancing the image content. However, performing these calculations on large datasets is computationally demanding and time-consuming even on high-end workstations. Especially in long-time measurements on developing animals, huge amounts of image data are acquired. To keep them manageable, redundancies should be removed right after image acquisition. To this end, we report a fast approximation to three-dimensional multi-view deconvolution, denoted 2D+1D multi-view deconvolution, which is able to keep up with the data flow. It first operates on the two dimensions perpendicular and subsequently on the one parallel to the rotation axis, exploiting the rotational symmetry of the point spread function along the rotation axis. We validated our algorithm and evaluated it quantitatively against two-dimensional and three-dimensional multi-view deconvolution using simulated and real image data. 2D+1D multi-view deconvolution takes similar computation time but performs markedly better than the two-dimensional approximation only. Therefore, it will be most useful for image processing in time-critical applications, where the full 3D multi-view deconvolution cannot keep up with the data flow

WARP: Wavelets with adaptive recursive partitioning for multi-dimensional data

Effective identification of asymmetric and local features in images and other data observed on multi-dimensional grids plays a critical role in a wide range of applications including biomedical and natural image processing. Moreover, the ever increasing amount of image data, in terms of both the resolution per image and the number of images processed per application, requires algorithms and methods for such applications to be computationally efficient. We develop a new probabilistic framework for multi-dimensional data to overcome these challenges through incorporating data adaptivity into discrete wavelet transforms, thereby allowing them to adapt to the geometric structure of the data while maintaining the linear computational scalability. By exploiting a connection between the local directionality of wavelet transforms and recursive dyadic partitioning on the grid points of the observation, we obtain the desired adaptivity through adding to the traditional Bayesian wavelet regression framework an additional layer of Bayesian modeling on the space of recursive partitions over the grid points. We derive the corresponding inference recipe in the form of a recursive representation of the exact posterior, and develop a class of efficient recursive message passing algorithms for achieving exact Bayesian inference with a computational complexity linear in the resolution and sample size of the images. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of 2D and 3D image reconstruction using real images from the ImageNet database. We also apply the framework to analyze a data set from retinal optical coherence tomography

Spectral transformation in the SOFI complex for processing photographic images on the ES computer, part 1

A description is given of three programs catalogued in the form of object modules in the library of a system for processing photographic images computer. PFT is the subprogram of the multi-dimensional BPF of real-valued information, in the operative computer memory. INRECO is a subprogram-interface between the real and complex formats for representing two-dimensional spectra and images. FFT2 is a subprogram for calculating the correlation functions of the image using the previous subprograms

A multilevel approach for nonnegative matrix factorization

Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing, computational biology, etc. In this paper, we explain how algorithms for NMF can be embedded into the framework of multi- level methods in order to accelerate their convergence. This technique can be applied in situations where data admit a good approximate representation in a lower dimensional space through linear transformations preserving nonnegativity. A simple multilevel strategy is described and is experi- mentally shown to speed up significantly three popular NMF algorithms (alternating nonnegative least squares, multiplicative updates and hierarchical alternating least squares) on several standard image datasets.nonnegative matrix factorization, algorithms, multigrid and multilevel methods, image processing

Separating true range measurements from multi-path and scattering interference in commercial range cameras

Time-of-flight range cameras acquire a three-dimensional image of a scene simultaneously for all pixels from a single viewing location. Attempts to use range cameras for metrology applications have been hampered by the multi-path problem, which causes range distortions when stray light interferes with the range measurement in a given pixel. Correcting multi-path distortions by post-processing the three-dimensional measurement data has been investigated, but enjoys limited success because the interference is highly scene dependent. An alternative approach based on separating the strongest and weaker sources of light returned to each pixel, prior to range decoding, is more successful, but has only been demonstrated on custom built range cameras, and has not been suitable for general metrology applications. In this paper we demonstrate an algorithm applied to both the Mesa Imaging SR-4000 and Canesta Inc. XZ-422 Demonstrator unmodified off-the-shelf range cameras. Additional raw images are acquired and processed using an optimization approach, rather than relying on the processing provided by the manufacturer, to determine the individual component returns in each pixel. Substantial improvements in accuracy are observed, especially in the darker regions of the scene

Using the SkelCL Library for High-Level GPU Programming of 2D Applications

Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches — CUDA and OpenCL — are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for single- and multi-GPU systems. In this paper, we focus on applications that work on two-dimensional data. We extend SkelCL by the matrix data type and the MapOverlap skeleton which specifies computations that depend on neighboring elements in a matrix. The abstract data types and a high-level data (re)distribution mechanism of SkelCL shield the programmer from the low-level data transfers between the system’s main memory and multiple GPUs. We demonstrate how the extended SkelCL is used to implement real-world image processing applications on two-dimensional data. We show that both from a productivity and a performance point of view it is beneficial to use the high-level abstractions of SkelCL