Real-time underwater image resolution enhancement using super-resolution with deep convolutional neural networks

Moghimi, Mohammad Kazem; Mohanna, Farahnaz

doi:10.1007/s11554-020-01024-4

Cited by 18 publications

(8 citation statements)

References 59 publications

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“…In order to show the efficiency of proposed algorithm, different images with various conditions were used to fulfill the peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM). The simulation results from different experiments revealed that the super-resolution in the used algorithm yields an accurate results for medium layers of underwater environments using the realtime algorithms [16].…”

Section: Related Workmentioning

confidence: 96%

An open-set framework for underwater image classification using autoencoders

Akhtarshenas

Toosi

2022

SN Appl. Sci.

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In this paper, we mainly intend to address the underwater image classification problem in an open-set scenario. Image classification algorithms have been mostly provided with a small set of species, while there exist lots of species not available to the algorithms or even unknown to ourselves. Thus, we deal with an open-set problem and extremely high false alarm rate in real scenarios, especially in the case of unseen species. Motivated by these challenges, our proposed scheme aims to prevent the unseen species from going to the classifier section. To this end, we introduce a new framework based on convolutional neural networks (CNNs) that automatically identifies various species of fishes and then classifies them into certain classes using a novel technique. In the proposed method, an autoencoder is employed to distinguish between seen and unseen species. To clarify, the autoencoder is trained to reconstruct the available species with high accuracy and filter out species that are not in our training set. In the following, a classifier based on EfficientNet is trained to classify the samples that are accepted by the autoencoder (AE), i.e. the samples that have small reconstruction error. Our proposed method is evaluated in terms of precision, recall, and accuracy and compared to the state-of-the-art methods utilizing WildFish dataset. Simulation results reveal the supremacy of the proposed method.

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Section: Related Workmentioning

confidence: 96%

An open-set framework for underwater image classification using autoencoders

Akhtarshenas

Toosi

2022

SN Appl. Sci.

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“…The software based approaches helps in recovering underwater images by utilising efficient algorithms. These software approaches can be further divided into image restoration method, colour correction methods, dark channel prior (DCP) based methods, fusion based methods and convolutional neural networks (CNNs) based methods [13][14][15][16][17][18][19][20][21][22][23].…”

Section: Literature Surveymentioning

confidence: 99%

“…Moghimi et al. [21] proposed a robust two‐step enhancement algorithm for underwater images. First step corrects the colour of underwater images and enhances the image quality by reducing hazing and darkening artefacts.…”

Section: Literature Surveymentioning

confidence: 99%

“…Further, underwater image enhancement network called Water-Net has been proposed and trained on this underwater image enhancement benchmark. Moghimi et al [21] proposed a robust two-step enhancement algorithm for underwater images. First step corrects the colour of underwater images and enhances the image quality by reducing hazing and darkening artefacts.…”

Section: Literature Surveymentioning

confidence: 99%

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Enhancement algorithm for high visibility of underwater images

Mathur

Goel

2021

IET Image Processing

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Underwater image enhancement has been attracting researchers in present day scenario for exploring the marine life. But underwater images suffer from various glitches like haziness, low contrast and faded colours due to absorption and scattering properties of light in water. To overcome these issues, the present paper proposes an enhancement method for underwater images. The proposed enhancement method comprises of three steps, i.e. automatic white balancing, dehazing and Rayleigh stretching in spatial domain. Automatic white balancing technique removes the colour cast from underwater images. Haze removal algorithm efficiently overcomes the haziness but it also reduces the local contrast and making the image appear dull. For improving the dehazing result and the visual quality simultaneously, the proposed method process the image by histogram stretching technique based on Rayleigh distribution. The proposed method corrects the colour cast and improves the contrast along with removing the haze from underwater images. Subjective and objective analysis on U 45 dataset validates the efficiency of proposed method and the enhanced images achieve better visual quality as compared to the existing methods. High values of EMEE, EME, UIQM and UCIQE for different types of underwater images further proves the potential and efficacy of the proposed method. INTRODUCTIONAn unexplored huge world present under oceans is attracting various researcher and scientists to study and investigate variety of marine life forms, incredible landscape, mysterious shipwrecks and beautiful coral reefs. Underwater environment is also gaining substantial attention due to increase in optical robotic applications and marine engineering [1,2]. But complex underwater environment and poor lighting conditions are major challenges to capture good quality underwater images. Water is a denser medium than the air and sunlight is reflected and refracted partially as it enters the ocean due to transition from one medium to the other. Less light will be reflected when ocean water is calm and smooth rather than when it is turbulent. The part of light which penetrates the ocean surface is affected by the phenomenon of refraction because light travels faster in air as compared to water. These refracted rays of light further interact with the water molecules and suspended solid particles leading to scattering and absorption. Scattering reduces intensity of light causing loss in contrast and haziness in underwater images. Whereas, absorption of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The authors of [17] investigated the problem of real-time underwater enhancement of super-resolution images. The authors have proposed a two-step method.…”

Section: Deep Computing and Neural Networkmentioning

confidence: 99%