124 research outputs found
Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction
Depth estimation from light field (LF) images is a fundamental step for some
applications. Recently, learning-based methods have achieved higher accuracy
and efficiency than the traditional methods. However, it is costly to obtain
sufficient depth labels for supervised training. In this paper, we propose an
unsupervised framework to estimate depth from LF images. First, we design a
disparity estimation network (DispNet) with a coarse-to-fine structure to
predict disparity maps from different view combinations by performing
multi-view feature matching to learn the correspondences more effectively. As
occlusions may cause the violation of photo-consistency, we design an occlusion
prediction network (OccNet) to predict the occlusion maps, which are used as
the element-wise weights of photometric loss to solve the occlusion issue and
assist the disparity learning. With the disparity maps estimated by multiple
input combinations, we propose a disparity fusion strategy based on the
estimated errors with effective occlusion handling to obtain the final
disparity map. Experimental results demonstrate that our method achieves
superior performance on both the dense and sparse LF images, and also has
better generalization ability to the real-world LF images
Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging
Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel
basis and generate spatiotemporal streaming events within microseconds in
response, offering visual information with high temporal resolution over a high
dynamic range. Such devices often serve in surveillance systems due to their
applicability and robustness in environments with high dynamics and strong or
weak lighting, where they can still supply clearer recordings than traditional
imaging. In other words, when it comes to privacy-relevant cases, neuromorphic
cameras also expose more sensitive data and thus pose serious security threats.
Therefore, asynchronous event streams also necessitate careful encryption
before transmission and usage. This letter discusses several potential attack
scenarios and approaches event encryption from the perspective of neuromorphic
noise removal, in which we inversely introduce well-crafted noise into raw
events until they are obfuscated. Evaluations show that the encrypted events
can effectively protect information from the attacks of low-level visual
reconstruction and high-level neuromorphic reasoning, and thus feature
dependable privacy-preserving competence. Our solution gives impetus to the
security of event data and paves the way to a highly encrypted technique for
privacy-protective neuromorphic imaging
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images
Light field (LF) images containing information for multiple views have
numerous applications, which can be severely affected by low-light imaging.
Recent learning-based methods for low-light enhancement have some
disadvantages, such as a lack of noise suppression, complex training process
and poor performance in extremely low-light conditions. To tackle these
deficiencies while fully utilizing the multi-view information, we propose an
efficient Low-light Restoration Transformer (LRT) for LF images, with multiple
heads to perform intermediate tasks within a single network, including
denoising, luminance adjustment, refinement and detail enhancement, achieving
progressive restoration from small scale to full scale. Moreover, we design an
angular transformer block with an efficient view-token scheme to model the
global angular dependencies, and a multi-scale spatial transformer block to
encode the multi-scale local and global information within each view. To
address the issue of insufficient training data, we formulate a synthesis
pipeline by simulating the major noise sources with the estimated noise
parameters of LF camera. Experimental results demonstrate that our method
achieves the state-of-the-art performance on low-light LF restoration with high
efficiency
Cross-Camera Human Motion Transfer by Time Series Analysis
Along with advances in optical sensors is the increasingly common practice of
building an imaging system with heterogeneous cameras. While high-resolution
(HR) video acquisition and analysis benefit from hybrid sensors, the intrinsic
characteristics of multiple cameras lead to a challenging motion transfer
problem. In this paper, we propose an algorithm using time series analysis for
motion transfer among multiple cameras. Specifically, we first identify
seasonality in the motion data, and then build an additive time series model to
extract patterns that could be transferred across different cameras. Our
approach has a complete and clear mathematical formulation, and the algorithm
is also efficient and interpretable. Through the experiment on real-world data,
we demonstrate the effectiveness of our method. Furthermore, our motion
transfer algorithm could combine with and facilitate downstream tasks, e.g.,
enhancing pose estimation on low-resolution (LR) videos with inherent patterns
extracted from HR ones.Comment: 10 pages, 9 figure
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