2,278 research outputs found
Deep Burst Denoising
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution
Quanta Burst Photography
Single-photon avalanche diodes (SPADs) are an emerging sensor technology
capable of detecting individual incident photons, and capturing their
time-of-arrival with high timing precision. While these sensors were limited to
single-pixel or low-resolution devices in the past, recently, large (up to 1
MPixel) SPAD arrays have been developed. These single-photon cameras (SPCs) are
capable of capturing high-speed sequences of binary single-photon images with
no read noise. We present quanta burst photography, a computational photography
technique that leverages SPCs as passive imaging devices for photography in
challenging conditions, including ultra low-light and fast motion. Inspired by
recent success of conventional burst photography, we design algorithms that
align and merge binary sequences captured by SPCs into intensity images with
minimal motion blur and artifacts, high signal-to-noise ratio (SNR), and high
dynamic range. We theoretically analyze the SNR and dynamic range of quanta
burst photography, and identify the imaging regimes where it provides
significant benefits. We demonstrate, via a recently developed SPAD array, that
the proposed method is able to generate high-quality images for scenes with
challenging lighting, complex geometries, high dynamic range and moving
objects. With the ongoing development of SPAD arrays, we envision quanta burst
photography finding applications in both consumer and scientific photography.Comment: A version with better-quality images can be found on the project
webpage: http://wisionlab.cs.wisc.edu/project/quanta-burst-photography
Recent Advances in Smartphone Computational Photography
Smartphone cameras present many challenges, most of which come from the need for them to be physically small. Their small size puts a fundamental limit on their ability to resolve detail and collect light, which makes low-light photography and zooming difficult. This paper presents two approaches to improve smartphone photography through software techniques. The first is handheld super-resolution which uses natural hand movement to improve the resolution smartphone images, especially when zoomed. The second approach is a system which improves low light photography in smartphones
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