A two-stage restoration of distorted underwater images using compressive sensing and image registration

Zhang, Zhen; Tang, Yugui; Yang, Kuo

doi:10.1007/s40436-020-00340-z

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Additionally, the part of the image containing the typical characteristics may have been incomplete due to the differences in shooting angle during the data acquisition, which may have affected the training effect of the network. (2) The water ripples generated by the swimming of koi may have affected the image definition, which resulted in the blurring and distortion of images, as well as other problems [ 42 ]. (3) The cross-breeding of koi reduces the differences between varieties, resulting in a situation where the offspring are neither like the characteristics of their mother fish nor the various characteristics of their father, which poses difficulties for classification.…”

Section: Discussionmentioning

confidence: 99%

KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

Zheng

Deng

Qi³

et al. 2022

Biology

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As the traditional manual classification method has some shortcomings, including high subjectivity, low efficiency, and high misclassification rate, we studied an approach for classifying koi varieties. The main contributions of this study are twofold: (1) a dataset was established for thirteen kinds of koi; (2) a classification problem with high similarity was designed for underwater animals, and a KRS-Net classification network was constructed based on deep learning, which could solve the problem of low accuracy for some varieties that are highly similar. The test experiment of KRS-Net was carried out on the established dataset, and the results were compared with those of five mainstream classification networks (AlexNet, VGG16, GoogLeNet, ResNet101, and DenseNet201). The experimental results showed that the classification test accuracy of KRS-Net reached 97.90% for koi, which is better than those of the comparison networks. The main advantages of the proposed approach include reduced number of parameters and improved accuracy. This study provides an effective approach for the intelligent classification of koi, and it has guiding significance for the classification of other organisms with high similarity among classes. The proposed approach can be applied to some other tasks, such as screening, breeding, and grade sorting.

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Section: Discussionmentioning

confidence: 99%

KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

Zheng

Deng

Qi³

et al. 2022

Biology

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“…The SPIHT compression algorithm for underwater images was proposed based on embedded coding compression and CS (Cai et al, 2019). Zhang et al (2021) used CS to overcome underwater image distortions. The CS multiscale entropy feature extraction method to process target radiation noise is efficient and accurate (Lei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

ESPC-BCS-Net: A network-based CS method for underwater image compression and reconstruction

Chen²,

Yu³

2023

Front. Mar. Sci.

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The Internet of Underwater Things (IoUT) is a typical energy-limited and bandwidth-limited system where the technical bottleneck is the asymmetry between the massive demand for information access and the limited communication bandwidth. Therefore, storing and transmitting high-quality underwater images is a challenging task. The data measured by cameras need to be effectively compressed before transmission to reduce storage and reconstruc-ted with minor errors, which is the best solution. Compressed sensing (CS) theory breaks through the Nyquist sampling theorem and has been widely used to reconstruct sparse signals accurately. For adaptive sampling underwater images and improving the reconstruction performance, we propose the ESPC-BCS-Net by combining the advantages of CS and Deep Learning. The ESPC-BCS-Net consists of three parts: Sampling-Net, ESPC-Net, and BCS-Net. The parameters (e.g. sampling matrix, sparse transforms, shrinkage thresholds, etc.) in ESPC-BCS-Net are learned end-to-end rather than hand-crafted. The Sampling-Net achieves adaptive sampling by replacing the sampling matrix with a convolutional layer. The ESPC-Net implements image upsampling, while the BCS-Net is used to image reconstruction. The efficient sub-pixel layer of ESPC-Net effectively avoids blocking artifacts. The visual and quantitative evaluation of the experimental results shows that the underwater image reconstruction still performs well when the CS ratio is 0.1 and the PSNR of the reconstructed underwater images is above 29.

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“…Correction is performed and finally the corrected image is optimized using polynomial expansion techniques of the image. Zhang et al used compressive sensing technology to perform preliminary restoration of video sequences, and then applied image registration technology to obtain the final result [9]. Both James and Zhang used compressive sensing to eliminate periodic large distortions in images, but the processing effect on random small distortions in images was poor, and there were problems such as loss of image details and blurring.…”

Section: Introductionmentioning

confidence: 99%

Underwater distorted image restoration algorithm based on compressed sensing and centroid method

Zeng

Feng

Gong

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Imaging of static scenes under water is widely used in underwater detection technology. Aiming at the problem of severe distortion of underwater scene images caused by water surface fluctuations. An underwater distorted image restoration algorithm based on compressed sensing and centroid method is proposed. First, track the significant feature points between video frames to obtain the displacement trajectory of the feature points, then use the compressed sensing algorithm to reconstruct the motion vector field of the entire image to eliminate the periodic distortion of the image. Further, on this basis, the dense optical flow between the images is calculated to construct the centroid of the image sequence, thereby eliminating the randomness distortion that may exist in the image. The experimental results show that compared with the current mainstream restoration methods, the algorithm can ensure that the restored image has high definition while eliminating image distortion, and it performs well in some image quality evaluation indicators.

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A two-stage restoration of distorted underwater images using compressive sensing and image registration

Cited by 8 publications

References 35 publications

KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

ESPC-BCS-Net: A network-based CS method for underwater image compression and reconstruction

Underwater distorted image restoration algorithm based on compressed sensing and centroid method

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