A small ISO and a small exposure time are usually used to capture an image in the back or low light conditions which results in an image with negligible motion blur and small noise but look dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the data-driven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of the MEF-SSIM metric.

中文翻译：

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通过混合学习的多尺度曝光融合实现单幅图像增亮

较小的 ISO 和较短的曝光时间通常用于在背光或低光条件下拍摄图像，这会导致图像运动模糊和噪点可忽略不计，但看起来很暗。在本文中，引入了单幅图像增亮算法来增亮这样的图像。所提出的算法包括一个独特的混合学习框架来生成两个具有大曝光时间的虚拟图像。虚拟图像首先通过使用相机响应函数 (CRF) 计算的强度映射函数 (IMF) 生成，这是一种模型驱动的方法。然后通过使用数据驱动的方法（即残差卷积神经网络）来增强这两个虚拟图像以接近地面实况图像。模型驱动的方法和数据驱动的方法在所提出的混合学习框架中相互补偿。通过具有适当定义权重的多尺度曝光融合算法，通过融合原始图像和两个虚拟图像获得最终的增亮图像。实验结果表明，所提出的增亮算法在 MEF-SSIM 度量方面优于现有算法。