Hybrid Saturation Restoration for LDR Images of HDR Scenes
This addresses the problem of image quality enhancement for users of smartphones and digital cameras, but it is incremental as it builds on existing model-based and data-driven methods.
The paper tackles the ill-posed problem of restoring saturated shadow and highlight regions in low dynamic range (LDR) images captured from high dynamic range (HDR) scenes by fusing model-based and data-driven approaches, resulting in an algorithm that can be embedded in smartphones or digital cameras to produce information-enriched LDR images.
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.