Abstract: This plenary speech presents an impulse noise removal approach which employs boundary discriminative noise detection with boundary resetting (BDNDBR) and polynomial interpolators. In the proposed approach, two stages are involved: noise detection and noise replacement. The noise detection performed by the BDNDBR is to identify a noisy pixel in an image. If a pixel is noise-free, then keep it intact. Or replace it with uncorrupted neighborhood pixels through the polynomial interpolators. Note that miss detection happens in the well-known BDND scheme when the noise density is high. The miss detection is even worse for cases with unbalanced noisy density where the portions for salt noise and pepper noise are different. To avoid the miss detection, a boundary resetting scheme is incorporated into the BDND. By this doing, the problem of miss detection in the BDND is prevented. In the noise replacement stage, two polynomial interpolators are adaptively selected to replace a noisy pixel according to the noise density. In the cases with higher noise density, a zero-order polynomial interpolator called adaptive nearest neighbor interpolator (ANNI) is used while a first-order polynomial interpolator called adaptive linear interpolator (ALI) is employed for the cases with lower noise density. Several examples are provided to justify the proposed BDNDBR, ANNI, and ALI. Moreover, the proposed noise removal approach is compared with other reported approaches as well.

Brief Biography of the Speaker:
Cheng-Hsiung Hsieh received his B.S. degree in Electronic Engineering from National Taiwan Institute of Technology, Taiwan, in 1989. In 1995, he earned the M.S. degree from the Department of Electrical Engineering of Tennessee Technological University, USA. He obtained his Ph.D. degree in Electrical Engineering from the University of Texas at Arlington, USA, in 1997. Currently, he is an associate professor at Department of Computer Science and Information Engineering in Chaoyang University of Technology, Taiwan. Since 1998, he has developed several grey models and other schemes applied to image, video, and speech signal processing. Those studies have been published in journals and conferences. Currently, his research interests are on image restoration, image enhancement, image enlargement, error concealment, and image/video coding.