PENERAPAN METODE CLAHE UNTUK MEMPERJELAS OBJEK PANTULAN KACA PADA CITRA DIGITAL

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sulharmi Irawan ◽  
Yasir Hasan ◽  
Kennedi Tampubolon
Keyword(s):  
Image Processing ◽  
Computer Graphics ◽  
Digital Image ◽  
Histogram Equalization ◽  
Image Objects ◽  
Adaptive Histogram Equalization ◽  
Image Displays ◽  
Reflection Image

Glass reflection image displays unclear or suboptimal visuals, such as overlapping images that blend with overlapping displays, so objects in images that have information and should be able to be processed for advanced research in the field of image processing or computer graphics do not give the impression so that research can be done. Improvement of overlapping images can be separated by displaying one of the image objects, the method that can be used is the Contras Limited Adaptive Histogram Equalization (CLAHE) method. CLAHE can improve the color and appearance of objects that are not clear on the image. Images that experience cases such as glass reflection images can be increased in contrast values to separate or accentuate one of the objects contained in the image using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method.Keywords: Digital Image, Glass Reflection, Contrast, CLAHE, YIQ.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

scholarly journals Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

2020 ◽  
Vol 5 (2) ◽  
pp. 53-60 ◽  
Author(s):  
Shoffan Saifullah
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Watershed Transform ◽  
Adaptive Histogram Equalization

Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Histogram Equalization (HE). Citra grayscale telur diperbaiki dengan menggunakan metode CLAHE, dan hasilnya diproses kembali dengan menggunakan HE. Hasil perbaikan citra menunjukkan bahwa metode kombinasi CLAHE-HE memberikan gambar secara jelas daerah objek citra telur yang memiliki embrio. Proses segmentasi dengan menggunakan konversi citra ke citra hitam putih dan segmentasi watershed mampu menunjukkan secara jelas objek telur ayam yang memiliki embrio. Hasil segmentasi mampu membagi daerah telur memiliki embrio secara nyata dan akurat dengan persentase sebesar  » 98%.

scholarly journals Implementation of HSV- based Thresholding Method for Iris Detection

2021 ◽  
Vol 3 (1) ◽  
pp. 98-104
Author(s):  
Fajrul Islami
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Input Image ◽  
Test Method ◽  
Threshold Method ◽  
Processing Stage ◽  
Image Thresholding ◽  
Image Objects ◽  
Intensity Changes

Image thresholding is one of the most frequently used methods in image processing to perform digital image processing. Image thresholding has a technique that can separate the image object from its background. This is a technique that is quite good and effective for segmenting love. In this study, the threshold method used will be combined with the HSV mode for color detection. The threshold method will separate the object and the image background, while HSV will help improve the segmentation results based on the Hue, Saturation, Value values to be able to detect objects more accurately. Segmentation is carried out using the original input image without pre-processing or direct segmentation. As we know that in digital image processing, there are steps that are usually done to get a good input image, namely pre-processing. In this pre-processing stage, processes such as image conversion and image intensity changes are carried out so that the input image is better. Therefore, even though the input image is used without going through the pre-processing stage, the object can be segmented properly based on the color type of the object. The results of this segmentation can later be used for recognition and identification of image objects. The results of the test method for object segmentation achieved a color similarity level of 25%, with an accuracy rate of 75% in detecting uniform color objects. So that this method can be one of the most effective methods in segmenting image objects without pre-processing or direct thresholding

APPLICATION OF MORPHOLOGICAL IMAGE PROCESSING TO REMOVE HAIR NOISES WHILE ENHANCING PATTERNS OF NEAR-INFRARED HUMAN VEIN IMAGES

2021 ◽  
pp. 69-75
Author(s):  
Thuan
Keyword(s):  
Image Processing ◽  
Near Infrared ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Structural Element ◽  
New Approach ◽  
Adaptive Histogram Equalization ◽  
Morphological Image Processing ◽  
Morphological Image ◽  
Processing Steps

Recently, the innovative vein finder system has been studied extensively and has many practical uses in healthcare and security. Developing a better vein finder system often relies on image processing procedures which help to enhance the vein images. Conventional image processing procedures as median filtering and adaptive histogram equalization have shown benefit in enhancing vein patterns. However, in some cases when there are hairs present in the images, most of these procedures are less effective in removing noises from hairs. In this work, we present a new approach employed additional morphological image processing procedures to efficiently remove hair noises. We have successfully constructed a vein finder device to acquire vein images and demonstrate the advantage of our approach. Effects of the size and shape of the structural element in different morphological image processing steps were studied and optimized to achieve the best enhancement effect. Our approach can be applied widely to other vein finder systems and enhance vein images from various parts of the human body.

Histogram Equalization Based on Rough Set

2012 ◽  
Vol 182-183 ◽  
pp. 1844-1848
Author(s):  
Peng Yan ◽  
Wu Zhan ◽  
Ou Yang Min-Zi
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Set Theory ◽  
Rough Set ◽  
Digital Image ◽  
Rough Set Theory ◽  
Histogram Equalization ◽  
Experimental Simulation ◽  
Gray Level ◽  
Approximate Measurement

In digital image processing, classical histogram equalization produce the loss of image information the caused by gray level of the output image may be too much merged. This paper mainly based on the concepts of the set approximate, classification approximate measurement and importance in the rough set theory, divided the appropriate boundary of the set, proposed an improved histogram equalization method, thus effectively solved the problem, gave the experimental simulation confirmation.

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