Improved Compressive Sensing Of Natural Scenes Using Localized Random Sampling

Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging

Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Ks band secondary eclipses of WASP-19b and WASP-43b with the Anglo-Australian Telescope

We report new Ks band secondary eclipse observations for the hot-Jupiters WASP-19b and WASP-43b. Using the IRIS2 infrared camera on the Anglo-Australian Telescope (AAT), we measured significant secondary eclipses for both planets, with depths of 0.287 -0.020/+0.020% and 0.181 -0.027/+0.027% for WASP-19b and WASP-43b respectively. We compare the observations to atmosphere models from the VSTAR line-by-line radiative transfer code, and examine the effect of C/O abundance, top layer haze, and metallicities on the observed spectra. We performed a series of signal injection and recovery exercises on the observed light curves to explore the detection thresholds of the AAT+IRIS2 facility. We find that the optimal photometric precision is achieved for targets brighter than Kmag = 9, for which eclipses as shallow as 0.05% are detectable at >5 sigma significance.Comment: Accepted for publication in MNRAS, 13 pages, 10 figure