  While neural networks-based photo processing solutions can provide a better
image quality compared to the traditional ISP systems, their application to
mobile devices is still very limited due to their very high computational
complexity. In this paper, we present a novel MicroISP model designed
specifically for edge devices, taking into account their computational and
memory limitations. The proposed solution is capable of processing up to 32MP
photos on recent smartphones using the standard mobile ML libraries and
requiring less than 1 second to perform the inference, while for FullHD images
it achieves real-time performance. The architecture of the model is flexible,
allowing to adjust its complexity to devices of different computational power.
To evaluate the performance of the model, we collected a novel Fujifilm
UltraISP dataset consisting of thousands of paired photos captured with a
normal mobile camera sensor and a professional 102MP medium-format FujiFilm
GFX100 camera. The experiments demonstrated that, despite its compact size, the
MicroISP model is able to provide comparable or better visual results than the
traditional mobile ISP systems, while outperforming the previously proposed
efficient deep learning based solutions. Finally, this model is also compatible
with the latest mobile AI accelerators, achieving good runtime and low power
consumption on smartphone NPUs and APUs. The code, dataset and pre-trained
models are available on the project website:
https://people.ee.ethz.ch/~ihnatova/microisp.html
