论文标题
可控的图像增强
Controllable Image Enhancement
论文作者
论文摘要
将外观的图像编辑成令人惊叹的照片需要技巧和时间。自动图像增强算法通过在没有用户互动的情况下生成高质量的图像来引起人们的兴趣。但是，照片的质量评估是主观的。即使在音调和颜色调整中，自动增强的一张照片对于适合用户偏好甚至可以改变的用户偏好也充满挑战。为了解决此问题，我们提出了一种半自动图像增强算法，该算法可以通过控制一些参数来生成具有多种样式的高质量图像。我们首先将照片修饰的技能与高质量图像相关，并为每种技能建立有效的增强系统。具体而言，编码器框架框架将修饰技能编码为潜在代码，并将它们解码为图像信号处理（ISP）函数的参数。 ISP函数在计算上是有效的，仅由19个参数组成。尽管我们需要多次推断才能获得所需的结果，但实验结果表明，所提出的方法在基准数据集上实现了最先进的性能，以达到图像质量和模型效率。
Editing flat-looking images into stunning photographs requires skill and time. Automated image enhancement algorithms have attracted increased interest by generating high-quality images without user interaction. However, the quality assessment of a photograph is subjective. Even in tone and color adjustments, a single photograph of auto-enhancement is challenging to fit user preferences which are subtle and even changeable. To address this problem, we present a semiautomatic image enhancement algorithm that can generate high-quality images with multiple styles by controlling a few parameters. We first disentangle photo retouching skills from high-quality images and build an efficient enhancement system for each skill. Specifically, an encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing (ISP) functions. The ISP functions are computationally efficient and consist of only 19 parameters. Despite our approach requiring multiple inferences to obtain the desired result, experimental results present that the proposed method achieves state-of-the-art performances on the benchmark dataset for image quality and model efficiency.
