Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

TL;DR

This paper introduces a guided conditional adversarial network that enhances flash images by removing harsh shadows, reconstructing overexposed areas, and generating natural ambient lighting for improved image quality.

Contribution

It presents a novel GAN-based method for ambient lighting generation and shadow removal in flash images, addressing gaps in previous normalization-focused approaches.

Findings

Outperforms baseline methods on FAID dataset

Effectively attenuates harsh shadows and reconstructs overexposed regions

Generates natural ambient shadows and scene tones

Abstract

To cope with the challenges that low light conditions produce in images, photographers tend to use the light provided by the camera flash to get better illumination. Nevertheless, harsh shadows and non-uniform illumination can arise from using a camera flash, especially in low light conditions. Previous studies have focused on normalizing the lighting on flash images; however, to the best of our knowledge, no prior studies have examined the sideways shadows removal, reconstruction of overexposed areas, and the generation of synthetic ambient shadows or natural tone of scene objects. To provide more natural illumination on flash images and ensure high-frequency details, we propose a generative adversarial network in a guided conditional mode. We show that this approach not only generates natural illumination but also attenuates harsh shadows, simultaneously generating synthetic ambient shadows. Our approach achieves promising results on a custom FAID dataset, outperforming our baseline studies. We also analyze the components of our proposal and how they affect the overall performance and discuss the opportunities for future work.