Enhancement of Infrared Images Based on Efficient Histogram Processing

Ashiba, Huda Ibrahim; Mansour, Hala M.; Ahmed, Hossameldin M.; El-Kordy, M.; Dessouky, Moawad I.; El‐Samie, Fathi E. Abd

doi:10.1007/s11277-017-4958-9

Cited by 34 publications

(6 citation statements)

References 3 publications

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“…Th he e " " à à t tr ro ou us s" " w wa av ve el le et t t tr ra an ns sf fo or rm m a al lg go or ri it th hm m It is a discrete approach of wavelet transform [16,20].The advantage of this algorithm is the shift invariance. This method was used in many applications such as signal analysis, data fusion and pattern recognition [21,22,23]. Our proposed approach uses à trous wavelet transform to decompose the image into wavelet planes.…”

Section: 2 2 Tmentioning

confidence: 99%

Fully Automated Fabric Defect Detection Using Additive Wavelet Transform

Saleh¹,

Fouad

Sayed

et al. 2020

Menoufia Journal of Electronic Engineering Research

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This paper introduces a proposed fabric defect detection technique based on additive wavelet transform. In this paper, à trous wavelet is utilized to extract the approximate sub image at an appropriate level. The objective of the proposed technique is to enhance energy of defective region and attenuate energy of background in the selected level. An improved thresholding method based on statistical calculation is used.

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Section: 2 2 Tmentioning

confidence: 99%

Fully Automated Fabric Defect Detection Using Additive Wavelet Transform

Saleh¹,

Fouad

Sayed

et al. 2020

Menoufia Journal of Electronic Engineering Research

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“…where k represents the number of unique intensities present in the image. The gray levels of the input and output (processed) images are denoted by g i and c i , respectively, with i = 0, 1… [27].…”

Section: Data Augmentation and Histogram Matchingmentioning

confidence: 99%

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Ignatius¹,

Selvakumar²,

Paul³

et al. 2023

Computer Systems Science and Engineering

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Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people's lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

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“…The results demonstrate that the proposed method improves the quality of the final fused image in terms of the MI (mutual information) and CC parameters. Another study presented in [25] enhanced the visibility of the IR (infrared) night vision images through an efficient histogram processing method that includes histogram equalization and matching. However, it is worth noting that the FS technique from "information" is pretty vital for studying machine learning recognition activities.…”

Section: Fs Issuesmentioning

confidence: 99%

Assessment for Different Neural Networks with FeatureSelection in Classification Issue

Chen

2022

Sensors

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In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. Supervised learning for NN originally comes from certain parameters, such as self-revised learning, input learning datasets, and multiple second learning processes. Specifically, the operation continues to adjust the NN connection synapses’ weight to achieve a self-learning computer system. The current article is aimed at developing the CC (correlation coefficient) assignment scheme adaptively joint with the FS (feature selection) categories to pursue the solutions utilized in solving the restrictions of NN computing. The NN computing system is expected to solve high-dimensional data, data overfitting, and strict FS problems. Hence, the Fruits-360 dataset is applied in the current article, that is, the variety of fruits, the sameness of color, and the differences in appearance features are utilized to examine the NN system accuracy, performance, and loss rate. Accordingly, there are 120 different kinds with a total of 20,860 fruit image datasets collected from AlexNet, GoogLeNet, and ResNet101, which were implemented in the CC assignment scheme proposed in this article. The results are employed to verify that the accuracy rate can be improved by reducing strict FS. Finally, the results of accuracy rate from the training held for the three NN frameworks are discussed. It was discovered that the GoogLeNet model presented the most significant FS performance. The demonstrated outcomes validate that the proposed CC assignment schemes are absolutely worthwhile in designing and choosing an NN training model for feature discrimination. From the simulation results, it has been observed that the FS-based CC assignment improves the accurate rate of recognition compared to the existing state-of-the-art approaches.

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Enhancement of Infrared Images Based on Efficient Histogram Processing

Cited by 34 publications

References 3 publications

Fully Automated Fabric Defect Detection Using Additive Wavelet Transform

Fully Automated Fabric Defect Detection Using Additive Wavelet Transform

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Assessment for Different Neural Networks with FeatureSelection in Classification Issue

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