43 research outputs found
4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images
Currently, mobile and IoT devices are in dire need of a series of methods to
enhance 4K images with limited resource expenditure. The absence of large-scale
4K benchmark datasets hampers progress in this area, especially for dehazing.
The challenges in building ultra-high-definition (UHD) dehazing datasets are
the absence of estimation methods for UHD depth maps, high-quality 4K depth
estimation datasets, and migration strategies for UHD haze images from
synthetic to real domains. To address these problems, we develop a novel
synthetic method to simulate 4K hazy images (including nighttime and daytime
scenes) from clear images, which first estimates the scene depth, simulates the
light rays and object reflectance, then migrates the synthetic images to real
domains by using a GAN, and finally yields the hazy effects on 4K resolution
images. We wrap these synthesized images into a benchmark called the 4K-HAZE
dataset. Specifically, we design the CS-Mixer (an MLP-based model that
integrates \textbf{C}hannel domain and \textbf{S}patial domain) to estimate the
depth map of 4K clear images, the GU-Net to migrate a 4K synthetic image to the
real hazy domain. The most appealing aspect of our approach (depth estimation
and domain migration) is the capability to run a 4K image on a single GPU with
24G RAM in real-time (33fps). Additionally, this work presents an objective
assessment of several state-of-the-art single-image dehazing methods that are
evaluated using the 4K-HAZE dataset. At the end of the paper, we discuss the
limitations of the 4K-HAZE dataset and its social implications
DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement
Low-light image enhancement is a classical computer vision problem aiming to
recover normal-exposure images from low-light images. However, convolutional
neural networks commonly used in this field are good at sampling low-frequency
local structural features in the spatial domain, which leads to unclear texture
details of the reconstructed images. To alleviate this problem, we propose a
novel module using the Fourier coefficients, which can recover high-quality
texture details under the constraint of semantics in the frequency phase and
supplement the spatial domain. In addition, we design a simple and efficient
module for the image spatial domain using dilated convolutions with different
receptive fields to alleviate the loss of detail caused by frequent
downsampling. We integrate the above parts into an end-to-end dual branch
network and design a novel loss committee and an adaptive fusion module to
guide the network to flexibly combine spatial and frequency domain features to
generate more pleasing visual effects. Finally, we evaluate the proposed
network on public benchmarks. Extensive experimental results show that our
method outperforms many existing state-of-the-art ones, showing outstanding
performance and potential
Non-invasive color imaging through scattering medium under broadband illumination
Due to the complex of mixed spectral point spread function within memory
effect range, it is unreliable and slow to use speckle correlation technology
for non-invasive imaging through scattering medium under broadband
illumination. The contrast of the speckles will drastically drop as the light
source's spectrum width increases. Here, we propose a method for producing the
optical transfer function with several speckle frames within memory effect
range to image under broadband illumination. The method can be applied to image
amplitude and color objects under white LED illumination. Compared to other
approaches of imaging under broadband illumination, such as deep learning and
modified phase retrieval, our method can provide more stable results with
faster convergence speed, which can be applied in high speed scattering imaging
under natural light illumination
One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration
In online reinforcement learning (online RL), balancing exploration and
exploitation is crucial for finding an optimal policy in a sample-efficient
way. To achieve this, existing sample-efficient online RL algorithms typically
consist of three components: estimation, planning, and exploration. However, in
order to cope with general function approximators, most of them involve
impractical algorithmic components to incentivize exploration, such as
optimization within data-dependent level-sets or complicated sampling
procedures. To address this challenge, we propose an easy-to-implement RL
framework called \textit{Maximize to Explore} (\texttt{MEX}), which only needs
to optimize \emph{unconstrainedly} a single objective that integrates the
estimation and planning components while balancing exploration and exploitation
automatically. Theoretically, we prove that \texttt{MEX} achieves a sublinear
regret with general function approximations for Markov decision processes (MDP)
and is further extendable to two-player zero-sum Markov games (MG). Meanwhile,
we adapt deep RL baselines to design practical versions of \texttt{MEX}, in
both model-free and model-based manners, which can outperform baselines by a
stable margin in various MuJoCo environments with sparse rewards. Compared with
existing sample-efficient online RL algorithms with general function
approximations, \texttt{MEX} achieves similar sample efficiency while enjoying
a lower computational cost and is more compatible with modern deep RL methods
Uncovering the Functional Link Between SHANK3 Deletions and Deficiency in Neurodevelopment Using iPSC-Derived Human Neurons
SHANK3 mutations, including de novo deletions, have been associated with autism spectrum disorders (ASD). However, the effects of SHANK3 loss of function on neurodevelopment remain poorly understood. Here we generated human induced pluripotent stem cells (iPSC) in vitro, followed by neuro-differentiation and lentivirus-mediated shRNA expression to evaluate how SHANK3 knockdown affects the in vitro neurodevelopmental process at multiple time points (up to 4 weeks). We found that SHANK3 knockdown impaired both early stage of neuronal development and mature neuronal function, as demonstrated by a reduction in neuronal soma size, growth cone area, neurite length and branch numbers. Notably, electrophysiology analyses showed defects in excitatory and inhibitory synaptic transmission. Furthermore, transcriptome analyses revealed that multiple biological pathways related to neuron projection, motility and regulation of neurogenesis were disrupted in cells with SHANK3 knockdown. In conclusion, utilizing a human iPSC-based neural induction model, this study presented combined morphological, electrophysiological and transcription evidence that support that SHANK3 as an intrinsic, cell autonomous factor that controls cellular function development in human neurons
Examining the generalizability of research findings from archival data
This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples