Image Visual Improvement on Handheld Devices Using Linear Mapping Function

In the current scenario, handheld devices play a major role in the human life. Handheld devices become an essential kit, not only acting as a conduit for social media, but also in medicine. Several new opportunities for the different applications of mobile image processing exist, such as to improve the visual quality, and image recognition. Captured images do not provide an effective visualization due to the poor specifications of the device camera, low light, poor sensing features, etc. In this article, an adaptive histogram equalization for contrast enhancement using a linear mapping function scheme is proposed to improve the images. The image from the mobile device is fed into a contrast improvement phase. The intensity value of each pixel is processed to improve the image visuals. The pixel density value is measured and according to it, the low-density value is changed. Hence, the image is tuned finely to yield better results.

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Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1667 ◽

2020 ◽

Vol 12 (2) ◽

pp. 80-88

Author(s):

Claudia Kenyta ◽

Daniel Martomanggolo Wonohadidjojo

Keyword(s):

Image Enhancement ◽

Light Condition ◽

Histogram Equalization ◽

Low Light ◽

Adaptive Histogram Equalization ◽

Enhancement Method ◽

Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

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Low Contrast Enhancement Technique for Color Images using Interval-Valued Intuitionistic Fuzzy Sets with Contrast Limited Adaptive Histogram Equalization

10.21203/rs.3.rs-618229/v1 ◽

2021 ◽

Author(s):

J Reegan Jebadass ◽

P Balasubramaniam

Keyword(s):

Fuzzy Sets ◽

Histogram Equalization ◽

Intuitionistic Fuzzy Sets ◽

Significant Shift ◽

Low Light ◽

Low Contrast ◽

Adaptive Histogram Equalization ◽

Intuitionistic Fuzzy ◽

Fuzzy Histogram ◽

Interval Valued

Abstract This work introduces a program to enhance images taken in low light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low light. In the proposed method, first the given low light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.Mathematics Subject Classification (2010) 68U10 · 94D05

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Computer Aided Classification and Detection of Leaf Disease using ANN

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8446.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1347-1353

Keyword(s):

Human Life ◽

Texture Features ◽

Normal Growth ◽

Histogram Equalization ◽

Diseased Leaf ◽

Plant Leaf ◽

Adaptive Histogram Equalization ◽

Artificial Neural Network Ann ◽

Ann Classifier

The contribution of a plant is highly important for both human life and environment. Diseases will affect plant, like all humans and animals. Various diseases may affect plant which disturbs the plants normal growth. Leaf, stem, fruit, root, and flower of the plant may get affected by these diseases. Without proper care the plant may die or its leaves, flowers, and fruits drop. Finding of such infections is required for exact distinguishing proof and treatment of plant sicknesses. The current technique for plant malady discovery utilizes human contribution for distinguishing proof and characterization of illnesses and these strategies endure with time-unpredictability. PC supported programmed division of illnesses from plant leaf utilizing delicate registering can be fundamentally valuable than the current techniques. In this paper, we proposed a method using Artificial neural network (ANN) for identification, classification and segmentation of diseases in plant leaf automatically. In the proposed system capturing the leaf images is done first and then contrast of the image is improved by using Contrast Limited Adaptive Histogram Equalization(CLAHE) method. Then, color and texture features are extracted from the segmented outputs and the ANN classifier is then trained by using that features and it could able to separate the healthy and diseased leaf samples properly. Exploratory outcomes demonstrate that the arrangement execution by ANN taking list of capabilities is better with an exactness of 98%.

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Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Symmetry ◽

10.3390/sym12091561 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1561

Author(s):

Changli Li ◽

Shiqiang Tang ◽

Jingwen Yan ◽

Teng Zhou

Keyword(s):

Image Enhancement ◽

State Of The Art ◽

Histogram Equalization ◽

Local Contrast ◽

Low Light ◽

Adaptive Histogram Equalization ◽

Light Image ◽

Image Capturing ◽

Enhancement Method ◽

Image Details

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

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Spatially Adaptive Histogram Equalization for Single Image-Based Ghost-free High Dynamic Range Imaging

TECHART Journal of Arts and Imaging Science ◽

10.15323/jais.2014.02.1.1.55 ◽

2014 ◽

Author(s):

Jaehyun Im

Keyword(s):

Dynamic Range ◽

High Dynamic Range ◽

Histogram Equalization ◽

High Dynamic Range Imaging ◽

Single Image ◽

Range Imaging ◽

Adaptive Histogram Equalization ◽

Spatially Adaptive ◽

High Dynamic

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PENERAPAN METODE CLAHE UNTUK MEMPERJELAS OBJEK PANTULAN KACA PADA CITRA DIGITAL

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v3i1.1563 ◽

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.

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