Yong Liu

I am a Ph.D. candidate in the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence at Xi'an Jiaotong University, where I am fortunate to be under the supervision of Prof. Fei Wang. Prior to my Ph.D. studies, I obtained the M.S. and B.S. degrees from the University of Electronic Science and Technology of China.

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Research

I'm interested in low-level computer vision, deep learning, generative diffusion models, and image processing. Now most of my research is about single image super-resolution and diffusion models.

We propose a deployment-friendly transformer unit namely UFONE (i.e., UnFolding ONce is Enough) and a Deployment-friendly Inner-patch Transformer Network (DITN) for the SISR task, which can achieve favorable performance with low latency and memory usage on both training and deployment platforms. Furthermore, to further boost the deployment efficiency, we provide an efficient substitution for layer normalization and propose a fusion optimization strategy for specific operators.

We propose a local multi-scale feature aggregation network, called LMFA-Net, which has a lightweight model structure and can be used for real-time dehazing. By learning the local mapping relationship between the clean value of a haze image at a certain point and its surrounding local region, LMFA-Net can directly restore the final haze-free image.

We propose a cross-channel fusion image dehazing network with feature attention (CFDN), which directly restores the final clear image from the hazy input. The network design is motivated by three strategies, namely cross-channel fusion, feature attention mechanism, and local residual learning. We show that they are effective for image dehazing problem.

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