Improving Retinal Image Quality Using the Contrast Stretching, Histogram Equalization, and CLAHE Methods with Median Filters

Erwin, Erwin

doi:10.5815/ijigsp.2020.02.04

Cited by 28 publications

(8 citation statements)

References 14 publications

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“…Higher MSE values indicate that there is a significant difference between original and distorted images. This implies that these images are of poor quality [ 16 ]. On the other hand, low MSE values are the preferred standard for quality images.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

African foods for deep learning-based food recognition systems dataset

Ataguba,

Ezekiel,

Daniel

et al. 2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

African foods for deep learning-based food recognition systems dataset

Ataguba,

Ezekiel,

Daniel

et al. 2024

Data in Brief

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“…This paper uses CLAHE 19 to enhance the image contrast, and CLAHE obtains more details of the image by improving the local contrast of the image. CLAHE clips the histogram with a pre‐defined threshold to change the slope of the cumulative histogram (CDF), which, in turn, affects the slope of the transform function to achieve the purpose of contrast clipping.…”

Section: Methodsmentioning

confidence: 99%

IRCM‐Caps: An X‐ray image detection method for COVID‐19

Qiu

2023

Clinical Respiratory J

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Objective COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. Methods The proposed model integrates CNN and CapsNet. And attention mechanism module and multi‐branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. Result The test dataset includes 1200 X‐ray images (400 COVID‐19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. Conclusion The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID‐19 X‐ray image dataset.

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“…To assess the effect of the change in a frame due to the encryption, PSNR [20] can be very useful in measuring the error between the encrypted and the original frames. The lower value of PSNR reflects the higher noise and hence the more degradation due to encryption.…”

Section: Peak Signal To Noise Ratio (Psnr)mentioning

confidence: 99%

Selective Video Encryption Using the Cross Coupling of One-dimensional Logistic Maps

Malladar¹,

Kunte²

2021

IJCNIS

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H.264 videos have been the most shared type of video format in recent times and hence its security is a major issue. The techniques presented in the recent times incur complex computations. The major research objective is to design an efficient Chaotic Selective Video Encryption (CSVE) technique which can result in a better visual degradation of the encrypted video with less computational complexity. In the proposed work, in order to secure the H.264 videos, two one-dimensional logistic maps are cross coupled in the chaotic encryption technique which uses a lookup table for data conversion. The technique is evaluated using different performance metrics like Peak Signal to Noise Ratio (PSNR), entropy, statistical analysis etc along with the traditional logistic map. The work is compared with some recent techniques in terms of PSNR and was found out that the proposed technique has better encryption effect.

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Improving Retinal Image Quality Using the Contrast Stretching, Histogram Equalization, and CLAHE Methods with Median Filters

Cited by 28 publications

References 14 publications

African foods for deep learning-based food recognition systems dataset

African foods for deep learning-based food recognition systems dataset

IRCM‐Caps: An X‐ray image detection method for COVID‐19

Selective Video Encryption Using the Cross Coupling of One-dimensional Logistic Maps

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