GDL

Journal from gdlhub / 2017-08-25 09:07:55
Oleh : Jing Rui Tang, Nor Ashidi Mat Isa, ITB
Dibuat : 2014-08-25, dengan 1 file

Keyword : contrast enhancement; detail preservation; grayscale image; histogram clipping; fuzzy membership
Url : http://journals.itb.ac.id/index.php/jictra/article/view/697
Sumber pengambilan dokumen : web

This paper modifies the Adaptive Contrast Enhancement Algorithm
with Details Preserving (ACEDP) technique by integrating a fuzzy element in
the image type selection. The proposed technique, named the Adaptive Fuzzy
Contrast Enhancement with Details Preserving (AFCEDP) technique, first
computes the degree of membership of the input image to three categories, i.e.
low-, middle- or high-level images. The AFCEDP technique then clips the
histogram at different plateau limits that are computed from both the degree of
membership and the clipping functions. The classification of an image in the
ACEDP technique is done based solely on the intensity range of the maximum
number of pixels, which may be inaccurate. In the proposed AFCEDP technique,
the image type classification is handled in a better way with the integration of a
fuzzy element. The performance of the proposed AFCEDP technique was
compared with the conventional ACEDP technique and several state-of-art
techniques described in the literature. The simulation results revealed that the
AFCEDP technique demonstrates good capability in contrast enhancement and
detail preservation. In addition, the experiments using cervical cell images and
HEp-2 cell images showed great potential of the AFCEDP technique as a
technique for enhancing medical microscopic images.

Deskripsi Alternatif :

This paper modifies the Adaptive Contrast Enhancement Algorithm
with Details Preserving (ACEDP) technique by integrating a fuzzy element in
the image type selection. The proposed technique, named the Adaptive Fuzzy
Contrast Enhancement with Details Preserving (AFCEDP) technique, first
computes the degree of membership of the input image to three categories, i.e.
low-, middle- or high-level images. The AFCEDP technique then clips the
histogram at different plateau limits that are computed from both the degree of
membership and the clipping functions. The classification of an image in the
ACEDP technique is done based solely on the intensity range of the maximum
number of pixels, which may be inaccurate. In the proposed AFCEDP technique,
the image type classification is handled in a better way with the integration of a
fuzzy element. The performance of the proposed AFCEDP technique was
compared with the conventional ACEDP technique and several state-of-art
techniques described in the literature. The simulation results revealed that the
AFCEDP technique demonstrates good capability in contrast enhancement and
detail preservation. In addition, the experiments using cervical cell images and
HEp-2 cell images showed great potential of the AFCEDP technique as a
technique for enhancing medical microscopic images.

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