Local Contrast-Based Pixel Ordering for Exact Histogram Specification

Inoue, Kohei; Hara, Kenji

doi:10.3390/jimaging8090247

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…• Adaptive Histogram Specification: It is technique which is employed to enhance the contrast of denoised CHF images. This technique tailors the contrast enhancement to local characteristics within the images, adapting to the specific features of different Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images regions [24]. The goal is to improve visibility and emphasize relevant details for more effective segmentation.…”

Section: Enhancement Of Denoised Chf Imagesmentioning

confidence: 99%

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Gupta

2024

EAI Endorsed Trans AI Robotics

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Precision agriculture heavily relies on accurately segmenting wheat canopies from chlorophyll fluorescence (CHF) images. However, these images often face challenges due to inherent noise and illumination variations, primarily induced by the thermal activity of photons emitting a fluorescence effect. The unique nature of fluorescence introduces variations in illumination, especially during the crop's dark adaptation before experimentation. This adaptation aims to capture the full fluorescence effect, starting from minimum fluorescence and progressing to maximum fluorescence. In the initial stages of fluorescence, images tend to appear darker compared to those progressing towards maximum fluorescence. This variability necessitates the development of a sophisticated hybrid approach to eliminate noise and enhance contrast collaboratively, maximizing the benefits derived from CHF images. This paper introduces a novel hybrid preprocessing approach designed to address these challenges. The proposed method integrates five denoising techniques, namely Discrete Cosine Transform, Block Matching-3D, Low-Rank Matrix Approximation, Wiener Filtering, and Median Filtering, to mitigate the impact of noise in CHF images. Simultaneously, two enhancement techniques, Adaptive Histogram Specification and Gamma Correction, are employed to accentuate critical features, compensating for inherent variations in illumination during the fluorescence process. The hybrid preprocessing technique was proposed after analysing different combinations of denoising and enhancement techniques. Through qualitative and quantitative analysis of the results, it was observed that Block Matching-3D denoising with Gamma Correction produced the best output, with an Average PSNR of 0.54 and Average MSE of 0.07. This cascaded approach not only emphasizes noise reduction but also prioritizes the enhancement of crucial information within CHF images. By synergistically combining denoising and enhancement methods, the proposed approach optimizes the overall quality of the images, laying a foundation for improved wheat canopy segmentation. This research contributes a comprehensive and innovative solution to the challenges associated with CHF images in precision agriculture. The proposed hybrid approach holds promise for advancing the accuracy and reliability of wheat canopy segmentation, thereby enhancing the efficacy of precision agricultural practices.

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Section: Enhancement Of Denoised Chf Imagesmentioning

confidence: 99%

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Gupta

2024

EAI Endorsed Trans AI Robotics

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“…Histogram-based features capture statistical information about images, allowing for efficient representation and comparison of visual data (Peeples et al, 2024). Researchers have explored different types of histograms, including grayscale histograms, RGB histograms, and HSV histograms, each offering unique information about image content and structure (Inoue et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification

Tiani Ayu Lestari,

Muhamad Fatchan,

Wahyu Hadikristanto

2024

IJSAS

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This study investigates the integration of image histograms with Convolutional Neural Networks (CNNs) using TensorFlow for gender classification. The research focuses on preprocessing techniques that significantly reduce the dimensionality of image data, enhancing computational efficiency model performance. Data augmentation methods, including rotation, shifting, and flipping, were applied to diversify the training dataset. The CNN model achieved high accuracy and validation accuracy, demonstrating its robustness. The findings reveal that the preprocessing steps effectively condensed the pixel to be 151,321 while retaining critical features for classification. The study underscores the potential applications of this methodology in security, marketing, and healthcare, where accurate gender classification is essential. Future research should explore more diverse datasets, advanced model architectures, and enhanced feature extraction methods to further improve performance. This research contributes to the field by offering a comprehensive approach to efficient and accurate gender classification, supported by robust data augmentation and preprocessing techniques.

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Local Contrast-Based Pixel Ordering for Exact Histogram Specification

Cited by 2 publications

References 24 publications

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification

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