Color computational ghost imaging based on a generative adversarial network

Ni, Yang; Zhou, Dingfu; Yuan, Sheng; Bai, Xing; Zhao, Xu; Chen, Jie; Liu, Cong; Zhou, Xin

doi:10.1364/ol.418628

Cited by 33 publications

(18 citation statements)

References 12 publications

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“…The authenticity of color is the main requirement of image storage and display. When processing color images, it is necessary to focus on solving the problem of color distortion [15,16]. Therefore, the processing of color images requires a higher level of skill than grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Jiao

2022

Journal of Sensors

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As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.

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Section: Introductionmentioning

confidence: 99%

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Jiao

2022

Journal of Sensors

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“…The generative adversarial network (GAN) has also been used for SPI, in which the discriminator introduces adversarial error into the loss function. The GAN-based method is able to achieve better results due to the advanced adversarial training strategy. − Other studies are mainly carried out from the aspects of learning method, , network structure, , and the combination with specific applications. − …”

Section: Introductionmentioning

confidence: 99%

VGenNet: Variable Generative Prior Enhanced Single Pixel Imaging

et al. 2023

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Single-pixel imaging (SPI) is an emerging imaging methodology that converts a two-or even three-dimensional image acquisition problem into a one-dimensional (1D) temporal-signal detection problem. Thus, it is crucially important to develop efficient SPI techniques for image reconstruction from the 1D measurements, in particular, an undersampled one. Recently, various studies have demonstrated the superiority of deep learning for SPI. However, due to the generalization issue, conventional datadriven deep learning is a task-specific approach. One needs to retrain the neural network for different SPI imaging problems and different types of objects. Here, we propose a variable generative network enhanced SPI algorithm (VGenNet) by incorporating a model-driven fine-tuning process into a generative model that may have been trained for other tasks. VGenNet simultaneously updates the input vector and the weights in a generator to generate feasible solutions that reproduce the raw measurements. We demonstrate the proposed technique with indoor SPI and outdoor 3D single-pixel LiDAR experiments. Our results show that high-quality images can be reconstructed at low sampling ratios under different system configurations, demonstrating the good performance and flexibility of VGenNet. Overall, the proposed VGenNet is a general framework to take advantage of both the data and physics priors, allowing the direct use of a pretrained generative model to solve various inverse imaging problems.

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“…In this approach, the image is produced by iterative minimization between the GAN-generated images and the measured data. The GAN can be used to produce a clear reconstruction from the noisy l 2 -norm solution [13,14]. Over the past years, this GAN approach for compressive imaging has seen a rapid development [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial network for super-resolution imaging through a fiber

Li,

Abrashitova,

Osnabrugge

et al. 2022

Preprint

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A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes.Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.

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Color computational ghost imaging based on a generative adversarial network

Cited by 33 publications

References 12 publications

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

VGenNet: Variable Generative Prior Enhanced Single Pixel Imaging

Generative adversarial network for super-resolution imaging through a fiber

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