18 research outputs found
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
Human visual system relies on both binocular stereo cues and monocular
focusness cues to gain effective 3D perception. In computer vision, the two
problems are traditionally solved in separate tracks. In this paper, we present
a unified learning-based technique that simultaneously uses both types of cues
for depth inference. Specifically, we use a pair of focal stacks as input to
emulate human perception. We first construct a comprehensive focal stack
training dataset synthesized by depth-guided light field rendering. We then
construct three individual networks: a Focus-Net to extract depth from a single
focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from
the focal stack, and a Stereo-Net to conduct stereo matching. We show how to
integrate them into a unified BDfF-Net to obtain high-quality depth maps.
Comprehensive experiments show that our approach outperforms the
state-of-the-art in both accuracy and speed and effectively emulates human
vision systems
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
GIA-Net: Global Information Aware Network for Low-light Imaging
It is extremely challenging to acquire perceptually plausible images under
low-light conditions due to low SNR. Most recently, U-Nets have shown promising
results for low-light imaging. However, vanilla U-Nets generate images with
artifacts such as color inconsistency due to the lack of global color
information. In this paper, we propose a global information aware (GIA) module,
which is capable of extracting and integrating the global information into the
network to improve the performance of low-light imaging. The GIA module can be
inserted into a vanilla U-Net with negligible extra learnable parameters or
computational cost. Moreover, a GIA-Net is constructed, trained and evaluated
on a large scale real-world low-light imaging dataset. Experimental results
show that the proposed GIA-Net outperforms the state-of-the-art methods in
terms of four metrics, including deep metrics that measure perceptual
similarities. Extensive ablation studies have been conducted to verify the
effectiveness of the proposed GIA-Net for low-light imaging by utilizing global
information.Comment: 16 pages 6 figures; accepted to AIM at ECCV 202
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks
© Springer Nature Switzerland AG 2018. We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image. Our novel convolutional architecture has a simultaneous view of all frames in the burst, and by construction treats them in an order-independent manner. This enables it to effectively detect and leverage subtle cues scattered across different frames, while ensuring that each frame gets a full and equal consideration regardless of its position in the sequence. We train the network with richly varied synthetic data consisting of camera shake, realistic noise, and other common imaging defects. The method demonstrates consistent state of the art burst image restoration performance for highly degraded sequences of real-world images, and extracts accurate detail that is not discernible from any of the individual frames in isolation
‘The things you didn’t do’: Gender, slut-shaming, and the need to address sexual harassment in narrative resources addressing sexting and cyberbullying
This chapter reports on research examining young people’s understandings of gender roles in everyday digital cultures and communication technologies, and in relation to sexting practices. A cyber-safety narrative film that addresses sexting, cyberbullying, and digital citizenship was used as a springboard for focus group discussions with 24 young people in Victoria, Australia. The chapter outlines the key findings regarding how young people understood and explained common gender dynamics in relation to bullying, cyberbullying, and sexting, reflecting as they did in these discussions on both the gender relations depicted in commonly used cyber-safety narrative resources, as well as in their own social lives. The chapter describes a discussion that arose among female participants around the ‘slut’ label, concerns about the possibility for sexual rumours to be spread via digital social networks, and associated on- and offline harassment over sexual things they had not actually done. This discussion, it is argued, illustrates the way girls feel responsible for protecting themselves from the potential psychic injuries of the slut label through strict sexual self-regulation, knowing that they cannot control malevolent and frequent use of this label by peers on- and offline. Future narrative resources that seek to address sexting and cyberbullying need to more clearly identify and respond to sexual harassment and sexism as a persistent feature of young people’s digital and school cultures