Low-light image enhancement method for UAV in night scenes
A simple and effective solution without task-relevant data is proposed,aiming at the problem of lowlight image enhancement for unmanned aerial vehicle (UAV)in night scenes. The method follows the image autoregressive principle and the grey-world color constancy hypothesis. It achieves high-quality enhancement of low-light images by constructing a Gaussian-distributed N(ηi,σi)sampling noise of the RGB channel and training an ultra-lightweight autoregressive model consisting of a five-layer convolutional network. The experimental results indicate that the proposed method is highly competitive in low-light image enhancement,as it enhances brightness and detail information and achieves good visual effects. Notably,the model is lightweight,and the millisecond-level inference speed is suitable for high-quality image enhancement of UAVs in low-light night scenes. Moreover,the proposed method is based on zero-sample learning,which requires no training data,thereby has good generalisation.
万方数据
维普
