Search Results for author:
Found 10 papers, 6 papers with code
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
A Simple Self-calibration Method for The Internal Time Synchronization of MEMS LiDAR
This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development.
Self-supervised Low Light Image Enhancement and Denoising
Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed.
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks
The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion.
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.
TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks
Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance.
Self-supervised Image Enhancement Network: Training with Low Light Images Only
We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning.
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring.
