Extracting Vignetting and Grain Filter Effects From Photos

Abdelrahman Abdelhamed, Jonghwa Yim, Abhijith Punnappurath, Michael S. Brown, Jihwan Choe, Kihwan Kim; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, pp. 1191-1199

Most smartphones support the use of real-time camera filters to impart visual effects to captured images. Currently, such filters come preinstalled on-device or need to be downloaded and installed before use (e.g., Instagram filters). Recent work [24] proposed a method to extract a camera filter directly from an example photo that has already had a filter applied. The work in [24] focused only on the color and tonal aspects of the underlying filter. In this paper, we introduce a method to extract two spatially varying effects commonly used by on-device camera filters---namely, image vignetting and image grain. Specifically, we show how to extract the parameters for vignetting and image grain present in an example image and replicate these effects as an on-device filter. We use lightweight CNNs to estimate the filter parameters and employ efficient techniques---isotropic Gaussian filters and simplex noise---for regenerating the filters. Our design achieves a reasonable trade-off between efficiency and realism. We show that our method can extract vignetting and image grain filters from stylized photos and replicate the filters on captured images more faithfully, as compared to color and style transfer methods. Our method is significantly efficient and has been already deployed to millions of flagship smartphones.