2016 JRNL PP Choongsang Cho - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Image smoothing has been used for image segmentation,
image reconstruction, object classification, and 3D content
generation. Several smoothing approaches have been used at the
pre-processing step to retain the critical edge, while removing
noise and small details. However, they have limited performance,
especially in removing small details and smoothing discrete
regions. Therefore, to provide fast and accurate smoothing, we
propose an effective scheme that uses a weighted combination of
the gradient, Laplacian, and diagonal derivatives of a smoothed
image. In addition, to reduce computational complexity, we
designed and implemented a parallel processing structure for the
proposed scheme on a graphics processing unit (GPU). For an
objective evaluation of the smoothing performance, the images
were linearly quantized into several layers to generate experimental
images, and the quantized images were smoothed using
several methods for reconstructing the smoothly changed shape
and intensity of the original image. Experimental results showed
that the proposed scheme has higher objective scores and better
successful smoothing performance than similar schemes, while
preserving and removing critical and trivial details, respectively.
For computational complexity, the proposed smoothing scheme
running on a GPU provided 18 and 16 times lower complexity
than the proposed smoothing scheme running on a CPU and the
L0-based smoothing scheme, respectively. In addition, a simple
noise reduction test was conducted to show the characteristics of
the proposed approach; it reported that the presented algorithm
outperforms the state-of-the art algorithms by more than 5.4 dB.
Therefore, we believe that the proposed scheme can be a useful
tool for efficient image smoothing.