DeepFlash: Turning a Flash Selfie into a Studio Portrait
arXiv:1901.04252 · doi:10.1016/j.image.2019.05.013
Abstract
We present a method for turning a flash selfie taken with a smartphone into a photograph as if it was taken in a studio setting with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in an ad-hoc acquisition campaign. Each pair consists of one photograph of a subject's face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend defects introduced by a close-up camera flash, such as specular highlights, shadows, skin shine, and flattened images.
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Conditional Generative Adversarial Nets
- Deep Bilateral Learning for Real-Time Image Enhancement
- HDR image reconstruction from a single exposure using deep CNNs
- TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation