Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have ...Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.展开更多
Although there has been a great breakthrough in the accuracy and speed of super-resolution(SR)reconstruction of a single image by using a convolutional neural network,an important problem remains unresolved:how to res...Although there has been a great breakthrough in the accuracy and speed of super-resolution(SR)reconstruction of a single image by using a convolutional neural network,an important problem remains unresolved:how to restore finer texture details during image super-resolution reconstruction?This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network(ELSRGAN),based on the Laplacian pyramid to capture the high-frequency details of the image.By combining Laplacian pyramids and generative adversarial networks,progressive reconstruction of super-resolution images can be made,making model applications more flexible.In order to solve the problem of gradient disappearance,we introduce the Residual-in-Residual Dense Block(RRDB)as the basic network unit.Network capacity benefits more from dense connections,is able to capture more visual features with better reconstruction effects,and removes BN layers to increase calculation speed and reduce calculation complexity.In addition,a loss of content driven by perceived similarity is used instead of content loss driven by spatial similarity,thereby enhancing the visual effect of the super-resolution image,making it more consistent with human visual perception.Extensive qualitative and quantitative evaluation of the baseline datasets shows that the proposed algorithm has higher mean-sort-score(MSS)than any state-of-the-art method and has better visual perception.展开更多
岩石薄片图像对研究石油地质特性以及油气勘探都有重要的意义。由于各种因素的限制,获取到的岩石薄片图像经常会出现分辨率较低的情况,一定程度上限制了研究者对其细节信息的掌握。而一般的神经网络超分辨率算法都需要大量的数据作为训...岩石薄片图像对研究石油地质特性以及油气勘探都有重要的意义。由于各种因素的限制,获取到的岩石薄片图像经常会出现分辨率较低的情况,一定程度上限制了研究者对其细节信息的掌握。而一般的神经网络超分辨率算法都需要大量的数据作为训练集,为了提升岩石薄片图像超分辨率重建算法纹理细节信息还原能力,本文利用单图像生成式对抗网络,不需输入大量数据集,对岩石薄片图像进行超分辨率重建。采用鄂尔多斯某油区岩石铸体薄片图像进行训练,通过峰值信噪比(Peak Signal to Noise Ratio,SSNR)和结构相似性(Structural Similarity,SSIM)评价指标进行模型评价,实验结果表明:该方法超分辨率处理的图像在视觉效果和评价指标上均具有良好的效果。展开更多
本文针对在低光照条件下图像分辨率低的问题,提出一种融合光照损失的图像超分辨率生成对抗网络(image super-resolution generative adversarial network based on light loss,LSRGAN)模型.该模型通过构建高分辨率-低分辨率图像对,利用...本文针对在低光照条件下图像分辨率低的问题,提出一种融合光照损失的图像超分辨率生成对抗网络(image super-resolution generative adversarial network based on light loss,LSRGAN)模型.该模型通过构建高分辨率-低分辨率图像对,利用生成器网络、判别器网络进行训练,实现低光照条件下更好的模型生成图像效果.该模型的损失函数包括光照损失、结构相似性损失、内容损失和对抗损失.模型通过构建光照损失函数,利用RGB三原色颜色空间与YIQ颜色空间的线性关系计算出图像中的亮度分量,将图像中的亮度作为损失函数,更好地恢复低光照条件下的低分辨率图像;通过增加结构相似性损失,计算超分辨率图像与真实高分辨率图像之间的结构相似性,提高生成图像的质量;内容损失区别于传统的基于像素的损失,使用VGG19网络中的特征映射进行计算,可以得到更逼真的生成图像;对抗损失使用判别器网络区分超分辨率图像与真实高分辨率图像,提高超分辨率图像的视觉效果.通过在4个标准数据集Set5、Set14、BSDS100和Urban100上设计对比实验,证明通过增加对光照更加敏感的损失函数,使该模型在低光照条件下具有更好的模型生成图像效果;同时通过增加结构相似性损失,使生成的图像视觉质量更好.展开更多
基金The authors are highly thankful to the Development Research Center of Guangxi Relatively Sparse-populated Minorities(ID:GXRKJSZ201901)to the Natural Science Foundation of Guangxi Province(No.2018GXNSFAA281164)This research was financially supported by the project of outstanding thousand young teachers’training in higher education institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory Breeding Base of System Control and Information Processing.
文摘Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.
基金This work was supported in part by the National Science Foundation of China under Grant 61572526.
文摘Although there has been a great breakthrough in the accuracy and speed of super-resolution(SR)reconstruction of a single image by using a convolutional neural network,an important problem remains unresolved:how to restore finer texture details during image super-resolution reconstruction?This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network(ELSRGAN),based on the Laplacian pyramid to capture the high-frequency details of the image.By combining Laplacian pyramids and generative adversarial networks,progressive reconstruction of super-resolution images can be made,making model applications more flexible.In order to solve the problem of gradient disappearance,we introduce the Residual-in-Residual Dense Block(RRDB)as the basic network unit.Network capacity benefits more from dense connections,is able to capture more visual features with better reconstruction effects,and removes BN layers to increase calculation speed and reduce calculation complexity.In addition,a loss of content driven by perceived similarity is used instead of content loss driven by spatial similarity,thereby enhancing the visual effect of the super-resolution image,making it more consistent with human visual perception.Extensive qualitative and quantitative evaluation of the baseline datasets shows that the proposed algorithm has higher mean-sort-score(MSS)than any state-of-the-art method and has better visual perception.
文摘岩石薄片图像对研究石油地质特性以及油气勘探都有重要的意义。由于各种因素的限制,获取到的岩石薄片图像经常会出现分辨率较低的情况,一定程度上限制了研究者对其细节信息的掌握。而一般的神经网络超分辨率算法都需要大量的数据作为训练集,为了提升岩石薄片图像超分辨率重建算法纹理细节信息还原能力,本文利用单图像生成式对抗网络,不需输入大量数据集,对岩石薄片图像进行超分辨率重建。采用鄂尔多斯某油区岩石铸体薄片图像进行训练,通过峰值信噪比(Peak Signal to Noise Ratio,SSNR)和结构相似性(Structural Similarity,SSIM)评价指标进行模型评价,实验结果表明:该方法超分辨率处理的图像在视觉效果和评价指标上均具有良好的效果。
文摘本文针对在低光照条件下图像分辨率低的问题,提出一种融合光照损失的图像超分辨率生成对抗网络(image super-resolution generative adversarial network based on light loss,LSRGAN)模型.该模型通过构建高分辨率-低分辨率图像对,利用生成器网络、判别器网络进行训练,实现低光照条件下更好的模型生成图像效果.该模型的损失函数包括光照损失、结构相似性损失、内容损失和对抗损失.模型通过构建光照损失函数,利用RGB三原色颜色空间与YIQ颜色空间的线性关系计算出图像中的亮度分量,将图像中的亮度作为损失函数,更好地恢复低光照条件下的低分辨率图像;通过增加结构相似性损失,计算超分辨率图像与真实高分辨率图像之间的结构相似性,提高生成图像的质量;内容损失区别于传统的基于像素的损失,使用VGG19网络中的特征映射进行计算,可以得到更逼真的生成图像;对抗损失使用判别器网络区分超分辨率图像与真实高分辨率图像,提高超分辨率图像的视觉效果.通过在4个标准数据集Set5、Set14、BSDS100和Urban100上设计对比实验,证明通过增加对光照更加敏感的损失函数,使该模型在低光照条件下具有更好的模型生成图像效果;同时通过增加结构相似性损失,使生成的图像视觉质量更好.