Cheng-Ming Chiang

TL;DR: In this article, an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs was developed.

TL;DR: This is the first paper that addresses all the deployment issues of image deblurring task across mobile devices, and is adopted by the championship-winning team in NTIRE 2020 Image Deblurring Challenge on Smartphone Track.

TL;DR: Gmalivenko et al. as mentioned in this paper proposed a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 seconds and producing high perceptual photo quality.

TL;DR: In this paper , the authors presented a novel micro-ISP model designed specifically for edge devices, taking into account their computational and memory limitations, which 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.

TL;DR: In this paper , the authors proposed an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption, using the REDS training dataset containing video sequences for a 4X video upscaling task.