论文标题
使用轻型CNN的非均匀照明图像的快速增强
Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs
论文作者
论文摘要
本文提出了一个新的轻卷卷积神经网络（5K参数），以同时有效地处理颜色，曝光，对比度，噪声和伪影等，以增强不均匀的照明图像图像。更具体地说，首先使用来自双重不同方面的Etinex模型（分别增强暴露不足和抑制过度曝光）首先增强输入图像。然后，将这两个增强的结果和原始图像融合在一起，以获得具有令人满意的亮度，对比度和细节的图像。最后，除去额外的噪声和压缩工件以获得最终结果。为了训练该网络，我们提出了一个半监督修饰的解决方案，并构建一个新的数据集（82K图像）包含各种场景和光条件。我们的模型可以实时增强0.5兆像素（例如600*800）图像，该图像比现有增强方法快。广泛的实验表明，我们的解决方案可以快速有效地处理不均匀的照明图像。
This paper proposes a new light-weight convolutional neural network (5k parameters) for non-uniform illumination image enhancement to handle color, exposure, contrast, noise and artifacts, etc., simultaneously and effectively. More concretely, the input image is first enhanced using Retinex model from dual different aspects (enhancing under-exposure and suppressing over-exposure), respectively. Then, these two enhanced results and the original image are fused to obtain an image with satisfactory brightness, contrast and details. Finally, the extra noise and compression artifacts are removed to get the final result. To train this network, we propose a semi-supervised retouching solution and construct a new dataset (82k images) contains various scenes and light conditions. Our model can enhance 0.5 mega-pixel (like 600*800) images in real time (50 fps), which is faster than existing enhancement methods. Extensive experiments show that our solution is fast and effective to deal with non-uniform illumination images.
