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Found 7 papers, 6 papers with code
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.
Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network
XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices.
HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation
Entertainment-oriented singing voice synthesis (SVS) requires a vocoder to generate high-fidelity (e. g. 48kHz) audio.
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.
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.
