Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network

Guo, Xiangji; Chen, Tao; Liu, Junchi; Liu, Yuan; An, Qichang; Jiang, Chunfeng

doi:10.3390/s23031086

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…The CandyCycleGAN network discriminator uses the PatchGAN (Luo et al 2021) [10] structure, where the PatchGAN output is an N × N matrix with two choices of true or false for each pixel in the matrix. The advantage of PatchGAN over traditional GAN discriminators is the ability to allow the generator to produce images with higher resolution, and better detailed images.…”

Section: Candycyclegan Discriminator Structurementioning

confidence: 99%

CandyCycleGAN: Candy Color Coloring Algorithm Based on Chromaticity Verification

2023

AJCIS

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Candy color is a new way of existence in the field of photography, and its characteristics of high brightness, low saturation, and low contrast bring a different color experience to the world. However, no relevant algorithm dedicated to candy color processing has been found yet, for this reason, the CandyCycleGAN network based on color verification is proposed to realize candy color recoloring. Based on the CycleGAN network, we implement multi-scale fusion to enhance the detailed features of the output image and improve the quality of the output image; we design the Chromaticity verification process to constrain the range of the generated chromaticity values to ensure that the final effect meets the expectation; we use the Smooth L 1 Loss as the loss function of the Chromaticity verification to measure the gap between the generated image and the expected image, and at the same time, compare the coloring quality of the image with that of the image using different loss functions; we add a gradient penalty to the coloring quality; we add a gradient penalty to the coloring quality. The gradient penalty is added to construct a new data distribution between the generated image and the expected image, and the gradient penalty is applied to each input data, which changes the gradient limitation method of the discriminator network and improves the stability of the network during the training process; the output of two different sizes of discriminant matrices allows the generator to generate images with higher resolution and better details. In comparison experiments with five algorithms such as CycleGAN, AdaAttN, etc., the CandyCycleGAN network reduces the computation by 37.95%, improves the PSNR by 49.83%, improves the SSIM by 54.77%, and improves the COLORFUL by 29.09% compared to the basic CycleGAN network model, and compared to the suboptimal AdaAttN model, the computation rises by 0.93%, but PSNR improves by 7.36%, SSIM improves by 7.14%, and COLORFUL improves by 17.30%. Comparative experimental results show that the proposed CandyCycleGAN network can achieve the optimal effect of high brightness, low saturation, and low contrast of candy color compared to the existing algorithms, which further validates the effectiveness of the algorithm.

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Section: Candycyclegan Discriminator Structurementioning

confidence: 99%

CandyCycleGAN: Candy Color Coloring Algorithm Based on Chromaticity Verification

2023

AJCIS

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“…Humidity sensors report on the relative humidity and air temperature. The GSM Module is a mechanism for mobile text messaging (Guo et al, 2023). Module for controlling power, measuring water levels, and detecting motion with a PIR sensor.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

Nisa,

Azam,

Rafiq

et al. 2024

ABBDM

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An Ontology-Based Smart Irrigation System is presented in this research with the goal of improving agricultural water management. The main issue discussed is the inefficiency of conventional irrigation techniques, which results in water waste and lower crop yields. The process used entails creating an ontology, which includes defining concepts, establishing relationships, identifying domains, and populating the ontology. In order to facilitate real-time monitoring and decision-making, the irrigation system also incorporates sensors, actuators, and data processing algorithms. The main conclusions show that the ontology-based approach boosts crop output, encourages sustainable farming practices, and enhances water consumption efficiency. The findings of this study imply that ontology-based smart irrigation systems present a viable way to deal with the problems associated with water scarcity, improve agricultural output, and reduce their negative effects on the environment. The research adds to the expanding corpus of information on intelligent irrigation systems and emphasizes the significance of implementing cutting-edge technologies for agriculture's sustainable water management.

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Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network

Cited by 2 publications

References 46 publications

CandyCycleGAN: Candy Color Coloring Algorithm Based on Chromaticity Verification

CandyCycleGAN: Candy Color Coloring Algorithm Based on Chromaticity Verification

Ontology-Based Smart Irrigation System: Enhancing Agricultural Water Management

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