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As the quality of mobile cameras starts to play a crucial role in modernsmartphones, more and more attention is now being paid to ISP algorithms usedto improve various perceptual aspects of mobile photos. In this Mobile AIchallenge, the target was to develop an end-to-end deep learning-based imagesignal processing (ISP) pipeline that can replace classical hand-crafted ISPsand achieve nearly real-time performance on smartphone NPUs. For this, theparticipants were provided with a novel learned ISP dataset consisting ofRAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor anda professional 102-megapixel medium format camera. The runtime of all modelswas evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AIprocessing unit capable of accelerating both floating-point and quantizedneural networks. The proposed solutions are fully compatible with the above NPUand are capable of processing Full HD photos under 60-100 milliseconds whileachieving high fidelity results. A detailed description of all models developedin this challenge is provided in this paper.

Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report