Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement

Paul, Abhisek

doi:10.1007/s00371-021-02330-z

Cited by 25 publications

(8 citation statements)

References 54 publications

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“…Similarly, HE can be used to improve image quality by increasing the contrast of the image. In this way, the image is better illuminated and highlighted (Paul, 2023). The WT and HE methods have been used for different applications in several studies in the literature.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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Purpose Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV). Design/methodology/approach Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV. Findings In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%. Originality/value An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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“…A common technique for improving images is called Histogram Equalization (HE), which modifies the grayscale histogram distribution and dynamic range to increase image contrast. [9][10][11] HE distributes pixel counts based on a computed transfer function; however, this may result in an overexpansion of the dynamic range. Mehdi and Shahidur developed Brightness Mean Preserving Bi-Histogram Equalization (BBHE) 12 to overcome the drawbacks of HE.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

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“…With the progress of information technology and the rapid development of image data acquisition technology, a large amount of multimedia data is generated every day in all walks of life, among which digital image data accounts for a large part [1][2][3][4][5][6]. Face with the explosive growth of digital image data, the traditional stand-alone mode of image processing has exposed many problems, such as slow processing speed and poor concurrency, etc.…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification of Massive Images Based on Cloud Computing Platform

Wang¹,

Cao²

2023

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With the continuously increasing requirement of data processing speed, it is more and more suitable for images to be processed on cloud computing platform with distributed parallel computing. Therefore, this paper studied the rapid classification of massive images based on cloud computing platform. The multi-mode information fusion of massive images was carried out, which gave the implementation steps of the proposed massive image fusion method based on information complementation. This paper proposed to classify the massive images based on salient interactive feedback network. The model constructed in this paper not only simplified the complicated network parameter initialization and tuning process, but also avoided the classification failure caused by the scarcity of labeled images, which is more suitable for application and practical application scenarios. The experimental results verified the effectiveness of the constructed model in rapidly classifying massive images.

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Adaptive tri-plateau limit tri-histogram equalization algorithm for digital image enhancement

Cited by 25 publications

References 54 publications

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Rapid Classification of Massive Images Based on Cloud Computing Platform

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