Objective

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The use of image super-resolution reconstruction technology implies the utilization of a set of low-quality low-resolution images (or motion sequences) to produce the corresponding high-quality and high-resolution ones.This technology has a wide range of applications in many fields

such as military

medicine

public safety

and computer vision.In the field of computer vision

image super-resolution reconstruction enables the image to transform from the detection level to the recognition level

and even advance to the identification level.In other words

image super-resolution reconstruction can enhance image recognition capability and identification accuracy.In addition

image super-resolution reconstruction involves a dedicated analysis of a target.In this analytic scheme

a comparatively high spatial resolution image of the region of interest is obtained instead of directly calculating the configuration of a high spatial resolution image by using large amounts of data.The conventional approaches of super-resolution reconstruction generally include example-based model

bi-cubic interpolation model

and sparse coding methods

among others.Deep learning has been considered for many associative subjects since the advent of artificial intelligence in recent years

and substantial research achievements have been realized in this field alongside the research on super-resolution reconstruction.Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have resulted in numerous breakthroughs and achievements in the domain of image super-resolution reconstruction.Examples include super-resolution reconstruction with CNN (SRCNN)

super-resolution reconstruction with very-deep convolutional networks (VDSR)

and super-resolution reconstruction with generative adversarial network (SRGAN).Particularly in SRGAN modeling

the single-image super-resolution technology has achieved remarkable progress

especially when the perceptual loss function instead of the traditional loss function based on the mean square error (MSE) is the optimization goal.The common problems during modeling can be effectively solved using the original loss function

and a relatively high peak signal-to-noise ratio (PSNR) can be obtained to resolve the fuzziness in the reconstruction results.However

even if super-resolution reconstruction can remarkably ameliorate image quality

a common problem is knowing how to comprehensively highlight the feature representation of reconstructed images

which then can improve the reconstruction quality of generated images.By itself

the method of super-resolution reconstruction causes an ill-posed problem; that is

images lose a certain amount of information during the down-sampling process.Therefore

the reconstruction of a high-resolution image may include the lost parts or characteristic of the corresponding low-resolution image

and this scenario inevitably leads to generative deviation.In addition

given that SRGAN does not add auxiliary trademark information into the loss function (i.e.

the model should have been explicitly instructed to generate the corresponding features)

the model may fail to accurately match the specific dimensions and semantic features of the data.Moreover

controllability will likely constrain the model from sufficiently representing the feature information of generated images

which then limits the model from improving the quality of reconstructed images.Such constraints pose difficulties to the subsequent identification and processing of the image.Aiming to solve the above problems

on the basis of the advantages of the SRGAN method

a super-resolution model based on the class-information generative adversarial network (class-info SRGAN) is proposed.Class-info SRGAN can be designed for the utilization of additional information variables to restrict the solution space scope of super-resolution reconstruction.Furthermore

class-info SRGAN can be used to assist the model to accurately fulfil the reconstruction task

particularly those referring to data semantic features.

Method

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The original SRGAN model involves the adding of a class classifier and integrating the class-loss item into the generative network loss.Then

back-propagation is employed during the training process to update the parameter weights of the network and provide feature class-information for the model.Finally

the reconstructed images are produced and possessed with the corresponding features.In contrast to the original objective function

the proposed model is innovative given its merits of having to introduce feature class-information and improving the optimization objective of the super-resolution model.Sequentially

it optimizes the network training process

and it then renders the feature representation of the reconstruction results to become more prominent.

Result

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According to the CelebA experiments

the class-loss item enables the SRGAN model to make minor changes and improve the output.A comparison of the SRGAN model with other models with gender-class information was conducted

and the differences were inconclusive

i.e.

it is hard to conclude whether the model has a significant effect even if improvements were achieved to some extent.The overall gender recognition rate of the generated images from the class-info SRGAN model ranges from 58% to 97%

which is higher than the rate of those from SRGAN (8% to 98%).However

with glasses-class information

the capability of the model to learn how to form better-shaped glasses increased.The results for the Fashion-mnist dataset and Cifar-10 dataset also show that the model has a significant effect even if the final results with the Cifar-10 dataset were not highly prominent as the previous experiments.In summary

the outcomes show that the reconstruction quality of the generated images from the class-info SRGAN model are better than those of the original SRGAN model.

Conclusion

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Class-information operates well in cases where the attributes are clear and the model has learned as much as possible.The experimental results verify the superiority and effectiveness of the proposed model in the super-resolution reconstruction task.On the basis of some concrete and simple feature attributes

class-info SRGAN will likely become a promising super-resolution model.However

to advance its application

the goals must be definite

e.g.

how to develop a general class-info SRGAN that can be used for various super-resolution reconstruction tasks

how to successfully conduct class-info SRGAN with multiple attributes simultaneously

and how to integrate auxiliary class-information into the architectures of class-info SRGAN efficiently and conveniently.These assumptions can provide references and conditions for acquiring better performing super-resolution reconstruction in the future.