Open AccessAnalysis of Sigmoid Function Method And Histogram Equalization for Enhancement Contrast Image
Author(s) -
Fadhillah Azmi,
Mawaddah Harahap
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1361/1/012013
Subject(s) - histogram equalization , adaptive histogram equalization , sigmoid function , histogram , equalization (audio) , computer science , brightness , histogram matching , contrast (vision) , image (mathematics) , artificial intelligence , image histogram , computer vision , balanced histogram thresholding , image quality , function (biology) , value (mathematics) , pattern recognition (psychology) , image processing , algorithm , color image , machine learning , physics , decoding methods , evolutionary biology , artificial neural network , optics , biology
Image Is one file that is able to provide information graphically to humans. However, not all images have a brightness level that suits the user, thus reducing the information obtained. To overcome this, it is necessary to increase contrast. The results of the tests show that the Histogram Equalization algorithm has an execution time of 83,109 ms, when compared to the time of the ACEBSF method which is 121.315ms. Whereas for the SSE value that is owned by the ACEBSF method is 14.619.460.211 greater than the SSE Histogram Equalization, which is 12.526.602.683. However, both methods have disadvantages if the K1 and K2 values are too small, the image quality improvement will fail.