Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network

Li, Li; Fan, Zijia; Zhao, Mingyang; Wang, Xinlei; Wang, Zhongyang; Guo, Longxiang

doi:10.1155/2020/8472875

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The experimental results and evaluations based on qualitative analysis, a user study, and standard quantitative metrics have been presented. The image resolutions have been compared by applying the existing algorithms such as SRGAN [7], ESRGAN [39], EDSRGAN [40], RSRGAN [24], ISGAN [25], SRDRMGAN [4] and Deep SESR [41].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The model inherits the traits of Super Resolution-GAN and aims to perform better in terms of perceptual metrics. Another model, the Image Sequence Generative Adversarial Network (ISGAN) [25], is an SR method based on aquatic image sequences obtained using multi-focus at similar angles. This method has made obtaining more details and thereby improving the resolution of the images possible.…”

Section: Gan-generative Adversarial Networkmentioning

confidence: 99%

“…6 shows the Performance comparison of AlphaSRGAN with other related GAN for super-resolution. [7], ESRGAN [39], EDSR-GAN [40], RSRGAN [24], ISGAN [25], SRDRMGAN [4], Deep SESR [41]…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Figure 6: Performance comparison of AlphaSRGAN with SRGAN[7], ESRGAN[39], EDSR-GAN[40], RSRGAN[24], ISGAN[25], SRDRMGAN[4], Deep SESR[41] …”

mentioning

confidence: 99%

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A Novel AlphaSRGAN for Underwater Image Super Resolution

Cherian¹,

Poovammal²

2021

Computers, Materials &Amp; Continua

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Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details. Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator. After the images are processed by the generator network, they are passed through an adversarial method for training models. The dataset used in this paper to learn Single Image Super Resolution (SISR) is the USR 248 dataset. Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality. Appraisal of images is done with reference to factors like local style information, global content and color. The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images-high (640 × 480) and low (80 × 60, 160 × 120, and 320 × 240). Paired instances of different sizes-2×, 4× and 8×-are also present in the dataset. Parameters like Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Underwater Image Quality Measure (UIQM) scores have been compared to validate the improved efficiency of our model when compared to existing works.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Gan-generative Adversarial Networkmentioning

confidence: 99%

“…6 shows the Performance comparison of AlphaSRGAN with other related GAN for super-resolution. [7], ESRGAN [39], EDSR-GAN [40], RSRGAN [24], ISGAN [25], SRDRMGAN [4], Deep SESR [41]…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Figure 6: Performance comparison of AlphaSRGAN with SRGAN[7], ESRGAN[39], EDSR-GAN[40], RSRGAN[24], ISGAN[25], SRDRMGAN[4], Deep SESR[41] …”

mentioning

confidence: 99%

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A Novel AlphaSRGAN for Underwater Image Super Resolution

Cherian¹,

Poovammal²

2021

Computers, Materials &Amp; Continua

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show abstract

“…Given a low-resolution (LR) image, the task of SISR is to generate a corresponding high-resolution (HR) instance with satisfying visual quality [1]. Image superresolution (SR) has been widely investigated in numerous applications, such as image inpainting [2], self-driving [3], pose detection [4], underwater image enhancement [5], video deinterlacing [6], and recognition [7]. Figure 1 shows an example of image super-resolution.…”

Section: Introductionmentioning

confidence: 99%

Landweber Iteration-Inspired Network for Image Super-Resolution

Wang

Liu

2022

Mathematical Problems in Engineering

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Image super-resolution (SR) is one of the classical ill-posed image processing issues to generate high-resolution (HR) images from given low-resolution (LR) instances. Recent SR works aim to find an elaborate convolutional neural network (CNN) design and regard it as an end-to-end filter to map the image from LR space to HR space. However, seldom of them concentrate on the mathematical proof of network design or consider the problem from an optimization perspective. In this paper, we investigate the image SR based on the Landweber iteration method, which is an effective optimization method to find a feasible solution for the ill-posed problem. By considering the issue from the optimization perspective, we design a corresponding Landweber iteration-inspired network to adaptively learn the parameters and find the HR results. Experimental results show the proposed network achieves competitive or better subjective and objective performance than other state-of-the-art methods with fewer parameters and computational costs.

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