Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network
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摘要: 针对低照度图像增强算法在实现细节增强的同时对噪声抑制考虑的不足问题，该文提出一种基于深度卷积神经网络的无参考低照度图像增强方法。首先，基于Retinex理论从输入的低照度图像中提取照射分量和反射分量，并分别对二者进行优化，随后将优化后的照射分量和反射分量相乘得到增强后的图像；同时，将3D块匹配(BM3D)的去噪效果融合进反射分量的优化过程中；最后，采用无参考图像训练的方式，并配合改进后的趋势一致性损失对网络参数进行更新。实验结果表明，该文算法相较于现有的主流算法，可有效地提升低照度图像的对比度和亮度，同时保持图像的自然性。Abstract: To address the shortcomings of existing low illumination image enhancement algorithms in achieving detail enhancement while considering noise suppression, a reference-free low-illumination image enhancement method based on deep convolutional neural networks is proposed in the paper. First, the illumination and reflection components are extracted from the input low-illumination image based on Retinex theory and optimised separately, after which the optimised illumination and reflection components are multiplied to obtain the enhanced image. loss to update the network parameters, meanwhile, the denoising effect of Block Matching 3D (BM3D ) is integrated into the optimization process of reflection components. The experimental results show that the algorithm in this paper can effectively enhance the contrast and brightness of low-illumination images compared to existing mainstream algorithms, while maintaining the naturalness of the images.
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Key words:
- Image enhancement /
- Low light /
- Retinex /
- Convolutional Neural Network(CNN) /
- Consistent trend
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