Abstract
Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, HSVNet, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.
Highlights
High dynamic range (HDR) photographs include rich color details by using higher color depths to encode a broad range of real-world luminance
We present a mask function derived from Saturation (S) and Value (V) information in HSV color space which can detect both over- and underexposed areas and suppress unnecessary information for each channel; 3
The main difference from previous HDR reconstruction models is using HSV information of images to allow the network to learn the relationships between saturated regions and colors
Summary
High dynamic range (HDR) photographs include rich color details by using higher color depths to encode a broad range of real-world luminance. When we take a picture in extremely bright scenery with some dark areas such as shadows under the sunlight or dark scenery with some bright areas such as the moon at night, the picture may partially contain very bright or dark areas. This results in missing details in over/underexposed areas of LDR images. We usually use exposure bracketing, which shoots multiple exposure images and merges them into an HDR image
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