Underexposed images are usually low in brightness and contrast, which degrade the performance of many computer vision algorithms. To solve the problem of overexposing areas that tend to be normal while recovering dark areas in low-light image enhancement tasks, we propose an image-to-patch enhancement model and design a lightweight convolutional neural network called PatchNet. Specifically, the new enhancement model indirectly enhances the network by introducing a patch image, which preserves the incremental information from the low-light image to the normal image. The incremental information is fused with the input image to recover the dark areas while protecting the normal areas of the image. Extensive experiments on real datasets demonstrate the advantages of our method over state-of-the-art methods in subjective feeling and objective evaluation. Our method has achieved better results in restoring details and the adjustment of brightness. By comparing to other methods, our method is more efficient.

中文翻译：

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PatchNet：一个微小的低光图像增强网络

曝光不足的图像通常亮度和对比度都很低，这会降低许多计算机视觉算法的性能。为了解决在低光图像增强任务中恢复暗区时容易过度曝光的问题，我们提出了一种图像到补丁增强模型，并设计了一种称为 PatchNet 的轻量级卷积神经网络。具体来说，新的增强模型通过引入补丁图像间接增强网络，保留了从低光图像到正常图像的增量信息。增量信息与输入图像融合以恢复暗区，同时保护图像的正常区域。在真实数据集上的大量实验证明了我们的方法在主观感受和客观评价方面优于最先进的方法。我们的方法在细节还原和亮度调整方面取得了较好的效果。通过与其他方法相比，我们的方法更有效。