Machine learning-based solutions for automatic image enhancement in real estate

Currently, image enhancements in the real estate market are tremendously labor-intensive to meet the high quality-standards and are outsourced to low-income countries. Those tasks include the rather simple enhancements like color and exposure correction but also range from changing the mood of images to even replace furniture. Even though the images are manually processed, the quality of the results show large variations and inconsistencies.

In this project, we aim to develop novel deep neural network tools to automatize several of the most re-occurring tasks in professional image copy-editing. Here, only the highest quality of outputs are sufficient for the professional setting and all images have to be processed in full camera resolution.

This is an industrial PhD project, conducted with our industry partner Esoft A/S.

Project Members

(Former group member)


(missing reference) (Marı́n-Vega Juan et al., 2022)
  1. Juan Marı́n-Vega, Michael Sloth, Peter Schneider-Kamp and Richard Röttger. DRHDR: A dual branch residual network for multi-bracket high dynamic range imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2022): 844–852. Link.