Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement's focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.

翻译：完全或半自动图像增强软件有助于用户提高照片的视觉吸引力,而不需要对手工图像编辑进行深入了解。然而,全自动方法通常以不给用户任何对优化过程的控制的黑盒方式增强图像,可能导致对用户不具有主观吸引力的图像进行编辑。半自动方法主要允许控制采用预先定义的编辑步骤,这限制了用户的创造力和进行详细调整的能力,例如亮度或对比度。我们争辩说,通过指导自动增强方法将用户偏好纳入用户的偏好,简化了图像编辑并增加了对用户的增强重点。因此,这项工作提议采用神经图像校正和改进Routine(NICER),这是一个神经网络,其基础是完全、半自动或完全手工的、不对用户有主观吸引力的进程中的不参照图像增强。NICER反复调整图像编辑参数,以便根据图像的风格和内容,最大限度地提高美感。用户的评分,可以随时修改这些参数,并引导优化进程走向一个理想的方向。我们这个互动网络将用户的用户互动导向提升到一个不鼓励用户提高图像的外地。我们通过新版本来改进用户对图像进行互动。