A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Fu, Qiaoyan; Zhang, Zhengbing; Celenk, Mehmet; Wu, Aibin

doi:10.3390/s18113954

Cited by 7 publications

(2 citation statements)

References 70 publications

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“…The images obtained by HRCT scanning of the patient's lung as the target area are processed by the HRCT image enhancement algorithm, and the obtained images are illustrated in Figure 1. The multihistogram equalization enhancement algorithm was selected to process the images [16], and the images processed by the two methods were compared. Results: compared with the multihistogram equalization enhancement algorithm, the image processed by the HRCT image enhancement algorithm was clearer, the resolution was higher, the gray level was higher, and the contrast was clearer.…”

Section: Processing Results and Evaluation Indicators Of Hrctmentioning

confidence: 99%

Intelligent Enhancement Algorithm-Based High Resolution CT Image in Evaluation of Expelling Wind and Relieving Cough and Asthma Prescription on Bronchial Asthma with High Immunoglobulin E Expression

Shen

Chen

2022

Computational and Mathematical Methods in Medicine

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The therapeutic effect of expelling wind and relieving cough and asthma prescription on bronchial asthma with high immunoglobulin E (IgE) expression was studied based on high resolution CT (HRCT) images of intelligent enhancement algorithm. 90 patients with bronchial asthma were randomly divided into control group ( n = 45 ) and therapy group ( n = 45 ). The control group was given budesonide inhalation. The therapy group was treated with expelling wind and relieving cough and asthma prescription on the basis of the treatment in the control group. Both groups were given 1 month of treatment time. The scanned images were processed by image enhancement algorithm, and the image quality and distribution separation measurement (DSM) were used as image evaluation indicators. The bronchial wall thickness (WT), inner diameter (L), wall area (WA), and percentage of wall area (WA%) were used as quantitative indicators of airway remodeling. The changes of traditional Chinese medicine (TCM) score before and after treatment were recorded and analyzed. The changes of serum IgE indicators before and after treatment were recorded. Results: the image quality of HRCT image enhancement algorithm was better, and the DSM was 36.7 larger than that of the control group. It was found that after 1 month of treatment, the TCM symptom score of the therapy group was lower than that of the control group ( P < 0.05 ), and compared with the control group, the IgE level of the therapy group was also reduced. Meanwhile, compared with the control group, the WT of segmental bronchial wall in the therapy group decreased, and the WT of left apical bronchus was smaller than that in the control group ( P < 0.05 ), and the WT of subsegmental bronchus decreased significantly ( P < 0.05 ). The L of segmental bronchus and subsegmental bronchus in the therapy group was wider ( P < 0.05 ). The WA of segmental bronchus and subsegmental bronchus decreased, but there was no statistical significance ( P > 0.05 ). Compared with the control group, the WA percentage of segmental bronchus and the WA percentage of subsegmental bronchus in the therapy group were significantly lower ( P < 0.05 ). In conclusion, image enhancement algorithm can improve the HRCT image quality of patients with bronchial asthma, which is helpful to improve the diagnostic efficiency in clinical treatment. Expelling wind and relieving cough and asthma prescription has a better therapeutic effect on bronchial asthma with high IgE expression.

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Section: Processing Results and Evaluation Indicators Of Hrctmentioning

confidence: 99%

Intelligent Enhancement Algorithm-Based High Resolution CT Image in Evaluation of Expelling Wind and Relieving Cough and Asthma Prescription on Bronchial Asthma with High Immunoglobulin E Expression

Shen

Chen

2022

Computational and Mathematical Methods in Medicine

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“…The part clipped is then equally distributed upon all histogram bins. The selected threshold is 2.0 in CLAHE which helps limit the height of the histogram [28]. This means that the slope of the cumulative distribution function curve will be reduced.…”

Section: 𝐹(𝑖 𝑗mentioning

confidence: 99%

An End-to-End CNN Approach for Enhancing Underwater Images Using Spatial and Frequency Domain Techniques

Rejal¹,

Nagaty²,

Pester³

2023

ATAIML

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Underwater image processing area has been a central point of interest to many people in many fields such as control of underwater vehicles, archaeology, marine biology research, etc. Underwater exploration is becoming a big part of our life such as underwater marine and creatures research, pipeline and communication logistics, military use, touristic and entertainment use. Underwater images are subject to poor visibility, distortion, poor quality, etc., due to several reasons such as light propagation. The real problem occurs when these images have to be taken at a depth which is more than 500 feet where artificial light needs to be introduced. This work tackles the underwater environment challenges such as as colour casts, lack of image sharpness, low contrast, low visibility, and blurry appearance in deep ocean images by proposing an end-to-end deep underwater image enhancement network (WGH-net) based on convolutional neural network (CNN) algorithm. Quantitative and qualitative metrics results proved that our method achieved competitive results with the previous work methods as it was experimentally tested on different images from several datasets.

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Adaptive histogram equalization in constant time

Härtinger,

Steger

2024

J Real-Time Image Proc

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Adaptive Histogram Equalization (AHE) and its contrast-limited variant CLAHE are well-known and effective methods for improving the local contrast in an image. However, the fastest available implementations scale linearly with the filter mask size, which results in high execution times. This presents an obstacle in real-world applications, where large filter mask sizes are desired while maintaining low execution times. In this work, we propose an efficient algorithm for AHE that reduces the per-pixel computational complexity to $$\mathcal {O}(1)$$ O ( 1 ) . To the best of our knowledge, this is the first time that a constant-time algorithm is proposed for AHE and CLAHE. In contrast to commonly used fast implementations, our method computes the exact result for each pixel without interpolation artifacts. We benchmark and compare our method to existing algorithms. Our experiments show that our method exhibits superior execution times independent of the filter mask size, which makes AHE and CLAHE fast enough to be usable in real-world applications.

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A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Cited by 7 publications

References 70 publications

Intelligent Enhancement Algorithm-Based High Resolution CT Image in Evaluation of Expelling Wind and Relieving Cough and Asthma Prescription on Bronchial Asthma with High Immunoglobulin E Expression

Intelligent Enhancement Algorithm-Based High Resolution CT Image in Evaluation of Expelling Wind and Relieving Cough and Asthma Prescription on Bronchial Asthma with High Immunoglobulin E Expression

An End-to-End CNN Approach for Enhancing Underwater Images Using Spatial and Frequency Domain Techniques

Adaptive histogram equalization in constant time

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