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
This paper proposes a technique of image content authentication based on the Laplacian Pyramid to verify the authenticity of image content.First,the image is decomposed into Laplacian Pyramid before the transformation...This paper proposes a technique of image content authentication based on the Laplacian Pyramid to verify the authenticity of image content.First,the image is decomposed into Laplacian Pyramid before the transformation.Next,the smooth and detail properties of the original image are analyzed according to the Laplacian Pyramid,and the properties are classified and encoded to get the corresponding characteristic values.Then,the signature derived from the encrypted characteristic values is embedded in the original image as a watermark.After the reception,the characteristic values of the received image are compared with the watermark drawn out from the image.The algorithm automatically identifies whether the content is tampered by means of morphologic filtration.The information of tampered location is p resented at the same time.Experimental results show that the proposed authentication algorithm can effectively detect the event and location when the original image content is tampered.Moreover,it can tolerate some distortions produced by compression,filtration and noise degradation.展开更多
The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this,the visual quality of underwater images is degraded beca...The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this,the visual quality of underwater images is degraded because of two main factors namely,backscattering and attenuation.Therefore,visual enhancement has become an essential process to recover the required data from the images.Many algorithms had been proposed in a decade for improving the quality of images.This paper aims to propose a single image enhancement technique without the use of any external datasets.For that,the degraded images are subjected to two main processes namely,color correction and image fusion.Initially,veiling light and transmission light is estimated tofind the color required for correction.Veiling light refers to unwanted light,whereas transmission light refers to the required light for color correction.These estimated outputs are applied in the scene recovery equation.The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques.The resultants are divided into three weight maps namely,luminance,saliency,chromaticity and fused using the Laplacian pyramid.The results obtained are graphically compared with their input data using RGB Histogram plot.Finally,image quality is measured and tabulated using underwater image quality measures.展开更多
文摘目的为了提高数字水印的鲁棒性和不可见性,提出一种基于Laplacian Pyramid和LWT-QR分解的水印算法。方法首先对宿主图像进行2层Laplacian Pyramid分解,取其第2层Laplacian残差图像进行一层LWT分解,取其低频子带进行大小为4×4的无重叠分块处理。然后,基于提升小波系数的相关属性,再对每个选中的低频子块进行QR分解,取分解后R矩阵的第1行为目标进行水印的嵌入,同时对水印进行Arnold置乱,置乱后的水印图像嵌入到R矩阵的第1行元素中。结果嵌入水印后图像的PSNR能够达到45 d B,而且该方案对常见的信号处理攻击有较好的鲁棒性,NC均值在0.9以上。结论理论分析和大量的实验数据表明,该方案能够很好地改善图像操作过程中的鲁棒性和不可见性。
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.
基金supported by the National Natural Science Foundation of China (Grant No.60573019)Guangdong Natural Science Foundation (No.05300198 and 05103541).
文摘This paper proposes a technique of image content authentication based on the Laplacian Pyramid to verify the authenticity of image content.First,the image is decomposed into Laplacian Pyramid before the transformation.Next,the smooth and detail properties of the original image are analyzed according to the Laplacian Pyramid,and the properties are classified and encoded to get the corresponding characteristic values.Then,the signature derived from the encrypted characteristic values is embedded in the original image as a watermark.After the reception,the characteristic values of the received image are compared with the watermark drawn out from the image.The algorithm automatically identifies whether the content is tampered by means of morphologic filtration.The information of tampered location is p resented at the same time.Experimental results show that the proposed authentication algorithm can effectively detect the event and location when the original image content is tampered.Moreover,it can tolerate some distortions produced by compression,filtration and noise degradation.
文摘The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this,the visual quality of underwater images is degraded because of two main factors namely,backscattering and attenuation.Therefore,visual enhancement has become an essential process to recover the required data from the images.Many algorithms had been proposed in a decade for improving the quality of images.This paper aims to propose a single image enhancement technique without the use of any external datasets.For that,the degraded images are subjected to two main processes namely,color correction and image fusion.Initially,veiling light and transmission light is estimated tofind the color required for correction.Veiling light refers to unwanted light,whereas transmission light refers to the required light for color correction.These estimated outputs are applied in the scene recovery equation.The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques.The resultants are divided into three weight maps namely,luminance,saliency,chromaticity and fused using the Laplacian pyramid.The results obtained are graphically compared with their input data using RGB Histogram plot.Finally,image quality is measured and tabulated using underwater image quality measures.
