Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Alam, Md. Shahinur; Erdenebat, Munkh-Uchral; Abbass, Mohammed Y.; Alam, Md. Ashraful; Kim, Nam

doi:10.3390/s21062164

Cited by 12 publications

(8 citation statements)

References 45 publications

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“…More recently, Ren et al [10] proposes a method inspired by [7] to circumvent the image SR problems, and Alam et al [11] propose an SRGAN approach, supported by model introduced by [3], for imaging microscopy. On the other hand, Lin et al [12] uses a SRGAN approach to circumvent the previous problem regarding multiple degradation on GANS, where the method idea is to use three discriminators, at the RD training, for improving the accuracy.…”

Section: Related Workmentioning

confidence: 99%

Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy

Moreira¹,

Romero²,

Ribeiro³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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The quality of images obtained from video surveillance systems is a decisive aspect when performing investigations at the Forensic Science. Features such as scars, tattoos, and skin marks are great examples of details that allow to consolidate an investigation at certain scenarios in which there is the necessity to identify individuals captured in a video or image footage. However, the low quality of images could affect the results of the investigations. In this sense, this work proposes the study of a computational model to address the problem of increasing the resolution of Low-Resolution (LR) images, also known as the problem of super-resolution of images. The main idea is to train a Generative Adversarial Network (GAN) so that it can be able to enhance low-quality images. The hypothesis is that a variant model of a GAN, named Super-Resolution Generative Adversarial Network (SRGAN), is capable to produce High-Resolution (HR) images from LR ones. The proposed methodology is based on experimental research with the aid of the hypothetical deductive method, where two well-recognised state of art methods were used, which proposes the use of convolutional neural networks and deep learning. For the model validation, were conducted four different experiments: two to avail the capacity of the GAN to produce images with enhanced resolution and two other experiments to evaluate the quality of the results produced by the SRGAN. The quantitative results of our experiments are promising, with performances that are similar to those obtained by state-of-the-art approaches. Moreover, the qualitative results based on performing a visual analysis of the images produced by our approach suggest a interesting performance in terms of visual quality.

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Section: Related Workmentioning

confidence: 99%

Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy

Moreira¹,

Romero²,

Ribeiro³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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“…However, the limitations of IIM make it difficult to use various approaches. The image reconstruction is improved by a method that involves an optical device as well as software-based interpolation methods [ 22 ]. Despite improving the software-based application of reconstructed images, these conventional methods are not able to produce satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter

Imtiaz

Hossain

Alam

et al. 2022

Sensors

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This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.

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“…When given a new input, the generator tries to produce an output that fools the discriminator. In biological image processing, cGANs are popular in multiple topics including data augmentation ( Bailo et al , 2019 ; Baniukiewicz et al , 2019 ; Dirvanauskas et al , 2019 ; Osokin et al , 2017 ), domain translation ( Han and Yin, 2017 ; Tang et al , 2020 ), resolution enhancement ( Alam et al , 2021 ; Ishii et al , 2020 ; Wang et al , 2022 ; Zhou et al , 2020 ), virtual stain ( Bayramoglu et al , 2017 ; Li et al , 2020 ; Liu et al , 2021 ; Rana et al , 2018 ; Rivenson et al , 2019 ; Vasiljević et al , 2021 ), stain normalization ( Cong et al , 2021 ; Zanjani et al , 2018 ) and others ( Isomura and Toyoizumi, 2021 ; Kench and Cooper, 2021 ; Wang et al , 2021 ). Particularly, Pix2Pix ( Isola et al , 2017 ) is a successful example of cGANs that show effectiveness on multiple tasks such as image colorization and style transfer.…”

Section: Introductionmentioning

confidence: 99%

Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN

Jiao

Jiang

et al. 2022

Bioinformatics

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Motivation While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins. Results We propose a Protein Localization Prediction (PLP) method using a cGAN named Four-dimensional Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions. Availability The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Cited by 12 publications

References 45 publications

Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy

Image Super Resolution Using Generative Adversarial Networks and non-Paired Strategy

Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter

Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN

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