Performance Analysis of HE Methods for Low Contrast Images

Sahoo, Subhasmita; Panda, Jagyanseni; Mohanty, Mihir Narayan

doi:10.1016/j.procs.2016.07.325

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The primary stage of the method involves the subdivision and equalization of the histogram. 33 The initial histogram is first divided into two sub-images, SL and SU, which cover a range of gray levels from 0 to X a À 1 and X a to LÀ1, respectively. This division is based on the exposure threshold value X a , which was computed in Equation (1).…”

Section: Image Enhancement Using Resihementioning

confidence: 99%

“…The quantity of recursions is based upon the disparity in exposure levels observed between consecutive iterations. The primary stage of the method involves the subdivision and equalization of the histogram 33 . The initial histogram is first divided into two sub‐images, SL and SU, which cover a range of gray levels from 0 to

{X}_a-1

and

{X}_a

to L−1, respectively.…”

Section: Proposed Research Modelmentioning

confidence: 99%

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Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies

Thavasimuthu,

Vidhya,

Sridhar

et al. 2024

Polymers for Advanced Techs

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The contamination of microplastics (MPs) creates a substantial risk to both the environment and human health, necessitating the development of efficient methods for detecting and categorizing these micro pollutant particles. As a solution, Dense‐UNet with Convolutional Vision Transformer (Dense‐UNet‐CvT), a novel deep learning (DL)‐based model is proposed to detect and classify the MPs by performing the computer vision tasks. The main objective of this work is to enhance the detection accuracy in detecting the MPs classified from the input images. Initially, a holographic MPs image dataset comprising primary classes such as polyethylene (PE), polystyrene (PS), low‐density polyethylene (LDPE), polyhydroxyalkanoate (PHA) is collected for training and evaluating the research model. The images from the dataset are preprocessed by performing image resizing, Recursive Exposure based Sub‐Image Histogram Equalization (RESIHE)‐based image enhancement, Gaussian Adaptive Bilateral Filtering (GABF)‐based denoising to improve the visual quality of the images. The preprocessed images are applied for segmentation using the Dense‐UNet model for performing semantic segmentation. The CvT model is implemented to extract useful features and to perform classification on detecting the known and unknown classes of MPs labeled in the collected dataset. The MPs detection and classification performances are computed in terms of detection rate, accuracy, f1‐score, and precision. The Dense‐UNet‐CvT model achieved 98.22% detection rate, 98.59% accuracy, 98.35% f1‐score, and 98.76% precision. These performances are compared with the current models for proper validation, in which the research model outperformed all the compared models in terms of performance. Overall, the proposed Dense‐UNet‐CvT model demonstrates superior performance across multiple evaluation metrics, suggesting its effectiveness in detecting and classifying MPs contamination in holographic images.

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Section: Image Enhancement Using Resihementioning

confidence: 99%

{X}_a-1

and

{X}_a

to L−1, respectively.…”

Section: Proposed Research Modelmentioning

confidence: 99%

Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies

Thavasimuthu,

Vidhya,

Sridhar

et al. 2024

Polymers for Advanced Techs

View full text Add to dashboard Cite

show abstract

“…The full reference measure the information of the enhanced image in comparison to original image. Similarly, PSNR [25] and AMBE [28] measure the information as a full reference to predict the noise and mean brightness error in the enhanced image. Whereas, entropy, as a no-reference metrics, measures the richness in the content of the information.…”

Section: B Quality Metricsmentioning

confidence: 99%