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.
翻译:将平向图像编辑成惊人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图像而吸引了更多的兴趣。 但是, 照片的质量评估是主观的。 即便在音调和颜色调整方面, 单张自动增强照片对于适应用户的偏好是很难的, 而这些偏好是微妙的, 甚至可以改变的。 为了解决这个问题, 我们提出了一个半自动图像增强算法, 它可以通过控制几个参数来生成具有多种风格的高质量图像。 我们首先将照片重触技能与高质量的图像分离, 并为每种技能建立一个高效的增强系统。 具体来说, 编码解码器- 框架将重组技能编码为潜在代码, 并将其解码到图像信号处理( ISP) 功能的参数中。 ISP 函数具有计算效率, 仅包含19个参数。 尽管我们的方法需要多重推论才能获得预期的结果, 但实验结果显示, 拟议的方法在图像质量和模型效率的基准数据集上取得了最先进的表现。