Hadamard ghost imaging with a small amount of mask plates based on a spread spectrum.

Ghost imaging techniques using low-cost bucket detectors have unrivaled advantages for some wavebands where plane array detectors are not available or where focusing is difficult. In these bands, fine mask plates are the key to implementing high-resolution and quality ghost imaging. However, manufacturing a large number of mask plates is necessary but undoubtedly expensive in traditional Hadamard ghost imaging (HGI).

Digging Into Uncertainty-Based Pseudo-Label for Robust Stereo Matching.

Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others. Such domain shift issue is usually addressed by substantial adaptation on costly target-domain ground-truth data, which cannot be easily obtained in practical settings. In this paper, we propose to dig into uncertainty estimation for robust stereo matching.

TUSR-Net: Triple Unfolding Single Image Dehazing with Self-Regularization and Dual Feature to Pixel Attention.

Single image dehazing is a challenging and illposed problem due to severe information degeneration of images captured in hazy conditions. Remarkable progresses have been achieved by deep-learning based image dehazing methods, where residual learning is commonly used to separate the hazy image into clear and haze components. However, the nature of low similarity between haze and clear components is commonly neglected, while the lack of constraint of contrastive peculiarity between the two components always restricts the performance of these approaches.

DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization.

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g.

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with other mental disorders, so the diagnosis of ADHD in children is very important. Electroencephalogram (EEG) is the sum of the electrical activity of local neurons recorded from the extracranial scalp or intracranial.

MLDA-Net: Multi-Level Dual Attention-Based Network for Self-Supervised Monocular Depth Estimation.

The success of supervised learning-based single image depth estimation methods critically depends on the availability of large-scale dense per-pixel depth annotations, which requires both laborious and expensive annotation process. Therefore, the self-supervised methods are much desirable, which attract significant attention recently. However, depth maps predicted by existing self-supervised methods tend to be blurry with many depth details lost.

Color computational ghost imaging based on a generative adversarial network.

A novel, to the best of our knowledge, color computational ghost imaging scheme is presented for the reconstruction of a color object image, which greatly simplifies the experimental setup and shortens the acquisition time. Compared to conventional schemes, it only adopts one digital light projector to project color speckles and one single-pixel detector to receive the light intensity, instead of utilizing three monochromatic paths separately and synthesizing the three branch results. Severe noise and color distortion, which are common in ghost imaging, can be removed by the utilization of a generative adversarial network, because it has advantages in restoring the image's texture details and generating the image's match to a human's subjective feelings over other generative models in deep learning.

The ApolloScape Open Dataset for Autonomous Driving and Its Application.

Autonomous driving has attracted tremendous attention especially in the past few years. The key techniques for a self-driving car include solving tasks like 3D map construction, self-localization, parsing the driving road and understanding objects, which enable vehicles to reason and act. However, large scale data set for training and system evaluation is still a bottleneck for developing robust perception models.

Treadmill exercise promotes angiogenesis in the ischemic penumbra of rat brains through caveolin-1/VEGF signaling pathways.

The purpose of this study was to investigate the role of caveolin-1 in treadmill-exercise-induced angiogenesis in the ischemic penumbra of rat brains, and whether caveolin-1 changes correlated with reduced brain injury induced by treadmill exercise, in rats after cerebral ischemia. Rats were randomized into five groups: sham-operated (S, n=7), model (M, n=36), exercise and model (EM, n=36), inhibitor and model (IM, n=36), and inhibitor, exercise, and model (IEM, n=36). Rats in the model groups underwent middle cerebral artery occlusion (MCAO).

Simultaneous transmission for an encrypted image and a double random-phase encryption key.

We propose a method to simultaneously transmit double random-phase encryption key and an encrypted image by making use of the fact that an acceptable decryption result can be obtained when only partial data of the encrypted image have been taken in the decryption process. First, the original image data are encoded as an encrypted image by a double random-phase encryption technique. Second, a double random-phase encryption key is encoded as an encoded key by the Rivest-Shamir-Adelman (RSA) public-key encryption algorithm.