Hybrid Saturation Restoration for LDR Images of HDR Scenes

TL;DR

This paper introduces a hybrid method combining model-based and data-driven techniques to restore saturated regions in LDR images of HDR scenes, enhancing image details in shadows and highlights.

Contribution

It proposes a novel exposedness aware saturation restoration network (EASRN) and a fusion strategy to improve LDR image quality from HDR scenes.

Findings

Effective restoration of shadow and highlight details.

Compatible with smartphones and digital cameras.

Improved visual quality of LDR images.

Abstract

There are shadow and highlight regions in a low dynamic range (LDR) image which is captured from a high dynamic range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this paper, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the model-based approach. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the high-light regions. Both synthetic images are then refined via a novel exposedness aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multi-scale exposure fusion algorithm. The proposed algorithm can be embedded in any smart phones or digital cameras to produce an information-enriched LDR image.