Real-time underwater image enhancement: a systematic review

Moghimi, Mohammad Kazem; Mohanna, Farahnaz

doi:10.1007/s11554-020-01052-0

Cited by 35 publications

(15 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is calculated to encode pixel intensity differences in the neighborhood region. Images must be of adequate quality for making the accurate predictions from the data [30][31][32] . The LBP value for the centre pixel must be calculated.…”

Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.

show abstract

Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The authors of [8] have provided a systematic review of real-time underwater image enhancement. One of the environments being researched and observed continuously by researchers is underwater locations.…”

Section: Conventional Methods Of Statistical Computingmentioning

confidence: 99%

Real-time statistical image and video processing for remote sensing and surveillance applications

Khosravi

Tavallali

2021

J Real-Time Image Proc

View full text Add to dashboard Cite

“…Then, the Maximum a Posteriori (MAP) have used to strengthen the edges in the color-corrected underwater image. Moghimi and Mohanna (2021) validate the techniques in different phases: hardware and software tools, different imaging practices, enhancing real-time image quality, focusing on definite objectives in imaging the underwater. They also compared numerous underwater image enhancement techniques in both qualitative and quantitative manner.…”

Section: Related Workmentioning

confidence: 99%

“…They also compared numerous underwater image enhancement techniques in both qualitative and quantitative manner. Depends upon the principle of algorithm, the techniques on visibility improvement of underwater images are surveyed (Bharati et al, 2018;Fayaz et al, 2021;Hashisho et al, 2019;Khamparia et al, 2021;Moghimi & Mohanna, 2021;Raveendran et al, 2021;Reggiannini & Moroni, 2021;Sharma et al, 2021;Wang et al, 2019Wang et al, , 2020Zhang et al, 2019) grouped under six-classes and their sub-classes are depicted in Fig. 2.…”

Section: Related Workmentioning

confidence: 99%

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Muthuraman

Santhanam

2021

Multidim Syst Sign Process

View full text Add to dashboard Cite

Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

show abstract

Real-time underwater image enhancement: a systematic review

Cited by 35 publications

References 114 publications

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Real-time statistical image and video processing for remote sensing and surveillance applications

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Contact Info

Product

Resources

About