King Hung Chiu

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Example-based super-resolution (SR) approaches mostly reconstruct and optimize the high-resolution (HR) image according to objective criteria such as imaging model. However, in consumer electronics applications the ultimate goal is better subjective visual effect rather than higher objective texture visibility. In this paper, we propose a SR method using(More)
Example-based super-resolution (SR) attracts great interest due to its wide range of applications. However, these algorithms usually involve patch search in a large database or the input image, which is computationally intensive. In this paper, we propose a scale-invariant self-similarity (SiSS) based super-resolution method. Instead of searching patches,(More)
As deep learning neural networks (DNNs) advance and increase in computational complexity, particularly in terms of memory cost, it becomes difficult to implement DNNs in fixed-point memory-sparse environments (e.g. integrated circuits in consumer electronics). Thus, the training of DNNs must be reformulated to balance the hardware costs needed to represent(More)
Film grain noise (FGN) is generated by the procedure of capturing pictures using photographic film. Images with FGN are subjectively pleasing. However, FGN is difficult to compress and its pleasant features are difficult to preserve when the images are resized. So in literature, FGN is extracted first, then regenerated for the processed noise-free images.(More)
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