scholarly journals Pengujian Modifikasi Kernel Konvolusi Untuk Penajaman Dan Penghalusan Citra Berwarna

Faktor Exacta ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 96
Author(s):  
Suriski Sitinjak
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Image Preprocessing ◽  
Digital Image Enhancement

<p>Suatu citra digital terkadang memiliki kualitas yang kurang baik, misalnya terdapat noise, terlalu tajam, kurang tajam, kabur, terlalu gelap, terlalu terang, dan sebagainya. Untuk itu diperlukan perbaikan kualitas citra digital (image enhancement) sebagai proses awal pengolahan citra (image preprocessing) agar citra dapat digunakan untuk proses selanjutnya, seperti pengenalan pola (patern recognition). Konvolusi merupakan salah satu teknik perbaikan kualitas citra yang digunakan untuk memanipulasi piksel-piksel dalam citra, sehingga diperoleh citra yang memiliki sifat dan informasi yang diinginkan. Penelitian ini bertujuan untuk melakukan eksperimen terhadap kernel/tapis konvolusi dengan beberapa pengujian untuk penajaman dan penghalusan citra berwarna. Hasil eksperimen/pengujian menunjukkan bahwa tingkat ketajaman suatu citra hasil konvolusi bergantung pada nilai tiap elemen dan ukuran matriks kernel. Semakin besar angka dan ukuran matriks yang digunakan maka semakin tinggi pula tingkat ketajaman yang dihasilkan. Pada penghalusan citra, tingkat kehalusan bergantung pada ukuran matriks kernel. Semakin besar ukuran matriks kernel maka semakin besar tingkat kehalusannya (semakin blur). Dari penelitian ini diharapkan dapat membantu mengetahui kernel yang tepat untuk memperbaiki kualitas suatu citra, sehingga dapat digunakan untuk proses lebih lanjut. <br />Kata kunci: konvolusi, kernel, citra digital</p>

Structure of the regular surface layer of Bacillus polymyxa

1983 ◽  
Vol 29 (7) ◽  
pp. 775-780 ◽  
Author(s):  
S. K. Burley ◽  
R. G. E. Murray
Keyword(s):  
Surface Layer ◽  
Image Enhancement ◽  
Digital Image ◽  
General Scheme ◽  
Bacillus Polymyxa ◽  
Surface Layers ◽  
Regular Surface ◽  
Protein Subunits ◽  
Digital Image Enhancement ◽  
Spore Forming Bacteria

The structure of the regular surface layer of Bacillus polymyxa has been examined to a resolution of 2.5–3.0 nm in electron micrographs of negatively stained preparations supplemented by optical-digital image enhancement. The layer is composed of morphological units, each consisting of four identical protein subunits, and has p4 symmetry with a = 10.0 nm. Within each unit cell are areas of high stain density of a limiting diameter of 2–3 nm, which are interpreted as holes penetrating the layer. A comparison is made with other tetragonal regular surface layers of spore-forming bacteria, B. sphaericus and Sporosarcina ureae, solved to 2.5-nm resolution. These layers are similar in the conformation of their protein units and in the distribution of the holes in the layer. A general scheme for the architecture of the tetragonal regular surface layers is derived.

Digital image enhancement for in vivo laser scanning microscopy

2005 ◽  
Vol 11 (4) ◽  
pp. 248-253 ◽  
Author(s):  
Martin J. Gruber ◽  
Alexandra Wackernagel ◽  
Erika Richtig ◽  
Silvia Koller ◽  
Helmut Kerl ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Laser Scanning ◽  
Laser Scanning Microscopy ◽  
Scanning Microscopy ◽  
Digital Image Enhancement

scholarly journals Digital Image Enhancement in Matlab: An Overview on Histogram Equalization and Specification

2018 ◽  
Author(s):  
Rezoana Bente Arif ◽  
Mohammad Mahmudur Rahman Khan ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Histogram Specification ◽  
Digital Image Enhancement

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

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