Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly used in many diverse applications. We address the problem of jointly recovering spatial and temporal scene radiance from very few photon counts. Our ConvNet-based scheme effectively combines spatial and temporal information present in measurements to reduce noise. We demonstrate that using our method one can acquire videos at a high frame rate and still achieve good quality signal-to-noise ratio. Experiments show that the proposed scheme performs quite well in different challenging scenarios while the existing approaches are unable to handle them.
Recent works in range imaging through SPAD sensors include @cite_35 @cite_30 @cite_7 . SPAD cameras have been used to perform challenging tasks such as transient imaging @cite_33 @cite_13 @cite_38 @cite_31 @cite_39 , non-line-of sight imaging @cite_40 @cite_22 @cite_28 @cite_27 and imaging through fog @cite_15 . Closely related to the topic of single photon counting is that of Binary Quanta image sensors @cite_11 @cite_37 . These sensors were developed with the objective of shrinking the pixel pitch. In Quanta imaging, the densely defined sensors oversample the scene radiance to generate binary measurements @cite_11 @cite_3 . Image reconstruction schemes have been proposed for these sensors @cite_46 @cite_1 @cite_23 @cite_21 . The main difference between these reconstruction methods and our scheme is that these algorithms consider the availability of a higher number of samples to predict the image intensity at a pixel location. These methods have mainly focused on recovering a static image. In contrast, our focus is on recovering videos.
@cite_30 @cite_35 @cite_22 @cite_3 @cite_15 @cite_38 @cite_21 @cite_39 @cite_23 @cite_46 @cite_37 @cite_7 @cite_28 @cite_40 @cite_27 @cite_33 @cite_1 @cite_31 @cite_13 @cite_11
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Single-photon avalanche diode (SPAD) based transient imaging suffers from an aberration called pile-up. When multiple photons arrive within a single repetition period of the illuminating laser, the SPAD records only the arrival of the first photon; this leads to a bias in the recorded light transient wherein the transient response at later time-instants are under-estimated. An unfortunate consequence of this is the need to operate the illumination at low-power levels to reduce the probability of multiple photons returning in a single period. Operating the laser at low power results in either low signal-to-noise ratio (SNR) in the measured transients or reduced frame rate due to longer exposure durations to achieve a high SNR. In this paper, we propose a signal processing-based approach to compensate pile-up in post-processing, thereby enabling high power operation of the illuminating laser. While increasing illumination does cause a fundamental information loss in the data captured by SPAD, we quantify this information loss using Cramer-Rao bound and show that the errors in our framework are only limited to this information loss. We experimentally validate our hypotheses using real data from a lab prototype. In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods. The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain, we encourage low-rank structure, whereas in the spatial domain, we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and fused multispectral images. A weighted version of the vector total variation norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low-resolution multispectral images by linear regression while the second one employs the principal component pursuit to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed split augmented Lagrangian shrinkage algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared with the state-of-the-art. Over the last decade, several techniques have been developed for looking around the corner by exploiting the round-trip travel time of photons. Typically, these techniques necessitate the collection of a large number of measurements with varying virtual source and virtual detector locations. This data is then processed by a reconstruction algorithm to estimate the hidden scene. As a consequence, even when the region of interest in the hidden volume is small and limited, the acquisition time needed is large as the entire dataset has to be acquired and then processed.In this paper, we present the first example of scanning based non-line-of-sight imaging technique. The key idea is that if the virtual sources (pulsed sources) on the wall are delayed using a quadratic delay profile (much like the quadratic phase of a focusing lens), then these pulses arrive at the same instant at a single point in the hidden volume – the point being scanned. On the imaging side, applying quadratic delays to the virtual detectors before integration on a single gated detector allows us to ‘focus’ and scan each point in the hidden volume. By changing the quadratic delay profiles, we can focus light at different points in the hidden volume. This provides the first example of scanning based non-line-of-sight imaging, allowing us to focus our measurements only in the region of interest. We derive the theoretical underpinnings of ‘temporal focusing’, show compelling simulations of performance analysis, build a hardware prototype system and demonstrate real results.