Wing-Shan Tam
Wing-Shan Tam has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- Chi-Wah Kok
Canaan Semiconductor Limited, Hong Kong, China
6 shared publications Chi-Wah Kok
Canaan Semiconductor Limited, Hong Kong, China
5 shared publications- Hei Wong
City University of Hong Kong
5 shared publications - Hing Cheung So
City University of Hong Kong
3 shared publications Chi Wah Kok
2 shared publications- Chi Wah Leung
Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
2 shared publications
Latest Publications
- 1 January 2022
- Published by Elsevier in Digital Signal Processing
- Vol. 120
Dual-band signal reconstruction based on periodic nonuniform sampling at optimal sampling rate
- 1 January 2022
- Published by Elsevier in Digital Signal Processing
- Vol. 120
Dual-band signal reconstruction based on periodic nonuniform sampling at optimal sampling rate
- 3 April 2020
- Published by Elsevier in Microelectronics Journal
- Vol. 100, 104759
CMOS low power split-drain MAGFET based magnetic field strength sensor
- 3 April 2020
- Published by Elsevier in Microelectronics Journal
- Vol. 100, 104759
CMOS low power split-drain MAGFET based magnetic field strength sensor
- 21 March 2020
- Published by Elsevier in Signal Processing
- Vol. 174, 107572
Second-order bandpass sampling with direct baseband signal reconstruction
- 21 March 2020
- Published by Elsevier in Signal Processing
- Vol. 174, 107572
Second-order bandpass sampling with direct baseband signal reconstruction
- 1 March 2020
- Published by Elsevier in Digital Signal Processing
- Vol. 98
Non-iterative and accurate frequency estimation of a single cisoid using two DFT coefficients
- 1 March 2020
- Published by Elsevier in Digital Signal Processing
- Vol. 98
Non-iterative and accurate frequency estimation of a single cisoid using two DFT coefficients
- Open Access
Image Denoising by Random Interpolation Average with Low-Rank Matrix Approximation
With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. This work presents a novel image denoising method that can tackle both impulsive noise, such as salt and pepper noise (SAPN), and additive white Gaussian noise (AWGN), such as hot carrier noise from CMOS sensor, at the same time. We propose to use low-rank matrix approximation to form the basic denoising framework, as it has the advantage of preserving the spatial integrity of the image. To mitigate the SAPN, the original noise corrupted image is randomly sampled to produce sampled image sets. Low-rank matrix factorization method (LRMF) via alternating minimization denoising method is applied to all sampled images, and the resultant images are fused together via a wavelet fusion with hard threshold denoising. Since the sampled image sets have independent but identical noise property, the wavelet fusion serves as the effective mean to remove the AWGN, while the LRMF method suppress the SAPN. Simulation results are presented which vividly show the denoised images obtained by the proposed method can achieve crystal clear image with strong structural integrity and showing good performance in both subjective and objective metrics.
Open AccessImage Denoising by Random Interpolation Average with Low-Rank Matrix Approximation
With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. This work presents a novel image denoising method that can tackle both impulsive noise, such as salt and pepper noise (SAPN), and additive white Gaussian noise (AWGN), such as hot carrier noise from CMOS sensor, at the same time. We propose to use low-rank matrix approximation to form the basic denoising framework, as it has the advantage of preserving the spatial integrity of the image. To mitigate the SAPN, the original noise corrupted image is randomly sampled to produce sampled image sets. Low-rank matrix factorization method (LRMF) via alternating minimization denoising method is applied to all sampled images, and the resultant images are fused together via a wavelet fusion with hard threshold denoising. Since the sampled image sets have independent but identical noise property, the wavelet fusion serves as the effective mean to remove the AWGN, while the LRMF method suppress the SAPN. Simulation results are presented which vividly show the denoised images obtained by the proposed method can achieve crystal clear image with strong structural integrity and showing good performance in both subjective and objective metrics.
- Open Access
- 23 February 2019
- Published by MDPI AG in Fractal and Fractional
- Vol. 3 (1) , 7
Fractal Image Interpolation: A Tutorial and New Result
This paper reviews the implementation of fractal based image interpolation, the associated visual artifacts of the interpolated images, and various techniques, including novel contributions, that alleviate these awkward visual artifacts to achieve visually pleasant interpolated image. The fractal interpolation methods considered in this paper are based on the plain Iterative Function System (IFS) in spatial domain without additional transformation, where we believe that the benefits of additional transformation can be added onto the presented study without complication. Simulation results are presented to demonstrate the discussed techniques, together with the pros and cons of each techniques. Finally, a novel spatial domain interleave layer has been proposed to add to the IFS image system for improving the performance of the system from image zooming to interpolation with the preservation of the pixel intensity from the original low resolution image.
- 23 February 2019
- Published by MDPI AG in Fractal and Fractional
- Vol. 3 (1) , 7
Open AccessFractal Image Interpolation: A Tutorial and New Result
This paper reviews the implementation of fractal based image interpolation, the associated visual artifacts of the interpolated images, and various techniques, including novel contributions, that alleviate these awkward visual artifacts to achieve visually pleasant interpolated image. The fractal interpolation methods considered in this paper are based on the plain Iterative Function System (IFS) in spatial domain without additional transformation, where we believe that the benefits of additional transformation can be added onto the presented study without complication. Simulation results are presented to demonstrate the discussed techniques, together with the pros and cons of each techniques. Finally, a novel spatial domain interleave layer has been proposed to add to the IFS image system for improving the performance of the system from image zooming to interpolation with the preservation of the pixel intensity from the original low resolution image.
- 1 December 2015
- Published by Elsevier in Microelectronics Journal
- Vol. 46 (12) , 1381
Radio frequency integrated circuit (RFIC)
- 1 December 2015
- Published by Elsevier in Microelectronics Journal
- Vol. 46 (12) , 1381
Radio frequency integrated circuit (RFIC)
- 1 December 2015
- Published by Elsevier in Microelectronics Journal
- Vol. 46 (12) , 1382-1386
Optimization of loss tangent and capacitor size of micro-vacuum dielectric capacitors
- 1 December 2015
- Published by Elsevier in Microelectronics Journal
- Vol. 46 (12) , 1382-1386
Optimization of loss tangent and capacitor size of micro-vacuum dielectric capacitors