基于多分支全卷积神经网络的低照度图像增强

Low-Light Image Enhancement Based on Multi-Branch All Convolutional Neural Network

针对低照度条件下图像对比度不高、颜色失衡和存在噪声等问题,提出了一种基于多分支全卷积神经网络(MBACNN)的低照度图像增强模型。该模型是一个端到端的模型,包含特征提取模块(FEM)、增强模块(EM)、融合模块(FM)和噪声提取模块(NEM)。通过对合成的低照度和高清图像样本进行训练,根据验证集的损失值不断调整模型参数,以得到最优模型;然后对合成低照度图像和真实低照度图像进行测试。实验结果表明,与传统的图像增强算法相比,所提出的模型能够有效提高图像对比度、调整颜色失衡并去除噪声,主观视觉和客观图像质量评价指标都得到进一步改善。

ing at the problems of low image contrast,color imbalance,and noise in low-light conditions,a low-light image enhancement model based on multi-branch all convolutional neural network(MBACNN)is proposed.The model is an end-to-end model,including feature extraction module(FEM),enhancement module(EM),fusion module(FM),and noise extraction module(NEM).By training the synthesized low-light and high-definition image sample,the model parameters are continuously adjusted according to the loss value of the verification set to obtain the optimal model,and then the synthetic low-light image and the real low-light image are tested.Experimental results show that compared with traditional image enhancement algorithms,the proposed model can effectively improve image contrast,adjust color imbalance,and remove noise.Both subjective visual and objective image quality evaluation indicators are further improved.