1,775 research outputs found
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
Image demosaicking and denoising are the two key fundamental steps in digital
camera pipelines, aiming to reconstruct clean color images from noisy luminance
readings. In this paper, we propose and study Wild-JDD, a novel learning
framework for joint demosaicking and denoising in the wild. In contrast to
previous works which generally assume the ground truth of training data is a
perfect reflection of the reality, we consider here the more common imperfect
case of ground truth uncertainty in the wild. We first illustrate its
manifestation as various kinds of artifacts including zipper effect, color
moire and residual noise. Then we formulate a two-stage data degradation
process to capture such ground truth uncertainty, where a conjugate prior
distribution is imposed upon a base distribution. After that, we derive an
evidence lower bound (ELBO) loss to train a neural network that approximates
the parameters of the conjugate prior distribution conditioned on the degraded
input. Finally, to further enhance the performance for out-of-distribution
input, we design a simple but effective fine-tuning strategy by taking the
input as a weakly informative prior. Taking into account ground truth
uncertainty, Wild-JDD enjoys good interpretability during optimization.
Extensive experiments validate that it outperforms state-of-the-art schemes on
joint demosaicking and denoising tasks on both synthetic and realistic raw
datasets.Comment: Accepted by AAAI202
On reducing mesh delay for peer-to-peer live streaming
Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to large group. In this paper, we address the design of overlay which achieves low source-to-peer delay, is robust to user churn, accommodates of asymmetric and diverse uplink bandwidth, and continuously improves based on existing user pool. A natural choice is the use of mesh, where each peer is served by multiple parents. Since the peer delay in a mesh depends on its longest path through its parents, we study how to optimize such delay while meeting a certain streaming rate requirement. We first formulate the minimum delay mesh problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge which serves as our benchmark and optimal solution for all the other schemes under comparison. Our heuristic makes use of the concept of power in network given by the ratio of throughput and delay. By maximizing the network power, our heuristic achieves very low delay. We then propose a simple distributed algorithm where peers select their parents based on the power concept. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation results show that our distributed protocol performs close to the centralized one, and substantially outperforms traditional and state-of-the-art approaches
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