My current research interests include generative models and computational photography.
I enjoy the research that can benefit real-world applications.
Some of my papers have been used in some commercial products.
A general-purpose backbone for image restoration tasks (e.g. denoising, deraining, and
A novel kernel basis attention (KBA) module has been proposed to effectively aggregate the
spatial information via a
series of learnable kernel bases.
A self-supervised image denoising method using noisy images and the noise model.
We achieve SOTA results on both real-world noise and synthetic noises (both point-wise and
spatial correlated noise types).
A method to synthesize "almost the most realistic" raw image noise by sampling directly from
the real noise distribution.
We also found existing comparisons of noise synthesis methods are based on inaccurate noise
We achieve over 93% winning rate of Protoss against the most difficult non-cheating
built-in AI (level-7) of Terran,
training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs.