Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity

A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter

A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented

Tensin links energy metabolism to extracellular matrix assembly.

The regulation of integrin function is key to fundamental cellular processes, including cell migration and extracellular matrix (ECM) assembly. In this issue, Georgiadou et al. (2017. J. Cell Biol. https://doi.org/10.1083/jcb.201609066) report that the metabolic sensor adenosine monophosphate-activated protein kinase influences tensin production to regulate α5β1-integrin and fibrillar adhesion assembly and thus reveal an important connection between energy metabolism and ECM assembly

Super-resolution Using Adaptive Wiener Filters

The spatial sampling rate of an imaging system is determined by the spacing of the detectors in the focal plane array (FPA). The spatial frequencies present in the image on the focal plane are band-limited by the optics. This is due to diffraction through a finite aperture. To guarantee that there will be no aliasing during image acquisiton, the Nyquist criterion dictates that the sampling rate must be greater than twice the cut-off frequency of the optics. However, optical designs involve a number of trade-offs and typical imaging systems are designed with some level of aliasing. We will refer to such systems as detector limited, as opposed to optically limited. Furthermore, with or without aliasing, imaging systems invariably suffer from diffraction blur, optical abberations, and noise. Multiframe super-resolution (SR) processing has proven to be successful in reducing aliasing and enhancing the resolution of images from detector limited imaging systems

Mission safety evaluation report for STS-31, postflight edition

Mission safety factors relative to Space Transportation System (STS) Mission 31 are discussed. In addition to a mission summary, safety risk factors and inflight anomalies are discussed

Phototransduction in Drosophila.

Phototransduction in Drosophila's microvillar photoreceptors is mediated by phospholipase C (PLC) resulting in activation of two distinct Ca(2+)-permeable channels, TRP and TRPL. Here we review recent evidence on the unresolved mechanism of their activation, including the hypothesis that the channels are mechanically activated by physical effects of PIP2 depletion on the membrane, in combination with protons released by PLC. We also review molecularly explicit models indicating how Ca(2+)-dependent positive and negative feedback along with the ultracompartmentalization provided by the microvillar design can account for the ability of fly photoreceptors to respond to single photons 10-100× more rapidly than vertebrate rods, yet still signal under full sunlight.The authors’ own research reviewed in the paper was supported by the Biotechnology and Biological Sciences Research Council (BBSRC Grants BB/D007585/1 and BB/G006865/1 to RCH; BB/H013849/1 to MJ), the State Key Laboratory of Cognitive Neuroscience and Learning open research fund to MJ, Jane and Aatos Erkko Foundation Fellowship to MJ, and the Leverhulme Trust grant (RPG-2012-567 to MJ).This is the accepted manuscript for a paper published in Current Opinion in Neurobiology Volume 34, October 2015, Pages 37–45, DOI: 10.1016/j.conb.2015.01.00

Accurate and robust image superresolution by neural processing of local image representations

Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method

Plant diseases : Sclerotinia disease in vegetables : control with Allisan fungicide : a progress report

During the past decade sclerotinia rot has become a major disease problem in metropolitan market gardens. The fungicide Allisan has given promising results as a cover spray for the control of Sclerotinia. Two applications of the material reduced the incidence of Sclerotinia in runner beans from 45 per cent, to 15 per cent, and in lettuce from 9 per cent, to 2 per cent