The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of , where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3 times on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.
A considerable body of work is dedicated to automatic photo enhancement. However, it traditionally only focused on a specific subproblem, such as super-resolution, denoising, deblurring, or colorization. All of these subproblems are tackled simultaneously when we generate plausible high-quality photos from low-end ones. Furthermore, these older works commonly train with artifacts that have been artificially applied to the target image dataset. Recreating and simulating all the flaws in one camera given a picture from another is close to impossible, therefore in order to achieve real-world photo enhancement we use the photos simultaneously captured by a capture rig from Ignatov @cite_7 . Despite their limitations, the related works contain many useful ideas, which we briefly review in this section.
@cite_7
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Automatic photo enhancement is one of the long-standing goals in image processing and computational photography. While a variety of methods have been proposed for manipulating tone and colour, most automatic methods used in practice, operate on the entire image without attempting to take the content of the image into account. In this paper, we present a new framework for automatic photo enhancement that attempts to take local and global image semantics into account. Specifically, our content-aware scheme attempts to detect and enhance the appearance of human faces, blue skies with or without clouds and underexposed salient regions. A user study was conducted that demonstrates the effectiveness of the proposed approach compared to existing auto-enhancement tools. © 2012 Wiley Periodicals, Inc.