NICER: Aesthetic Image Enhancement with Humans in the Loop

• URL: http://arxiv.org/abs/2012.01778v1
• Date: Thu, 3 Dec 2020 09:14:10 GMT
• Title: NICER: Aesthetic Image Enhancement with Humans in the Loop
• Authors: Michael Fischer, Konstantin Kobs, Andreas Hotho
• Abstract summary: This work proposes a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process. We show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes.
• Score: 0.7756211500979312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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