Daisuke
Iso
Profile
Daisuke is a senior research scientist at Sony AI. He has been working on imaging and sensing technology for more than 10 years in both academia and industry. His research interests are computational photography, computer vision and machine learning. He received the B.E, M.E, and Ph.D. degrees in information and computer science from Keio University, Tokyo, Japan, in 2001, 2003, and 2006, respectively. He was a visiting scholar at Columbia University from 2011 to 2013.
Message
“I’m in the Imaging & Sensing project and my mission is to fully maximize image sensor capabilities combining AI technologies. Sony Semiconductor Solutions is the market leader in CMOS image sensor, and they own powerful sensors and sensing technologies. I believe the fusion of AI, sensor and sensing technologies will revolutionize the imaging systems, which can be beyond human perception.”
Publications
Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images. Training these models, however, necessitates large labeled RAW datasets, which are costly and o…
Noise synthesis is a promising solution for addressing the data shortage problem in data-driven low-light RAW image denoising. However, accurate noise synthesis methods often necessitate labor-intensive calibration and profiling procedures during preparation, preventing them…
We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical quality, filling a gap in …
Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The res…
Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…
Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…
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