针对在原始图像中嵌入大量的水印(认证水印和参考水印)容易造成图像失真的问题,提出嵌入少量参考水印的方法。为了减小图像失真,提出将少量的参考水印信息嵌入在原始图像的边缘、轮廓等区域的方法。在恢复篡改区域时,提出利用图像修复...针对在原始图像中嵌入大量的水印(认证水印和参考水印)容易造成图像失真的问题,提出嵌入少量参考水印的方法。为了减小图像失真,提出将少量的参考水印信息嵌入在原始图像的边缘、轮廓等区域的方法。在恢复篡改区域时,提出利用图像修复算法将篡改块匹配问题转化为非局部自相似图像块的最小化问题,复制攻击图像本身的相似信息来恢复篡改区域。实验结果表明,在水印容量为0.015626的情况下,该算法得到嵌入水印后图像的峰值信噪比(peak signal to noise ratio,PSNR)均值超过60 dB,恢复图像的PSNR均值超过40 dB。展开更多
The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information perform...The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information performs well in repairing seriously damaged images,but it has bad performances when the images have the abundant structure information.The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed.The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images.After adopting the preference of Criminisi priority,the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image.The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness,and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.展开更多
In general there is a digital image with noise, low contrast, blnrred edges and other defects. To effectively enhance the contrast of the image blur to meet the requirements of the subsequent identification and detect...In general there is a digital image with noise, low contrast, blnrred edges and other defects. To effectively enhance the contrast of the image blur to meet the requirements of the subsequent identification and detection. This paper presents a fuzzy adaptive image contrast enhancement algorithm based on gray entropy. This method not only enhances the overall image contrast, but also effectively enrich the target image detail information, and suppress the noise amplification. Meanwhile, the paper proposes an improved K and P parameters image restoration algorithm. The algorithm combines both isotropic and anisotropic diffusion, the use of regional differences in the frequency achieved in the different regions use different iterative equation. Experimental results show that the algorithm with TV model algorithm compared with the same premise of restorative effects, avoiding the staircase effect and better than the TV model repair speed.展开更多
文摘针对在原始图像中嵌入大量的水印(认证水印和参考水印)容易造成图像失真的问题,提出嵌入少量参考水印的方法。为了减小图像失真,提出将少量的参考水印信息嵌入在原始图像的边缘、轮廓等区域的方法。在恢复篡改区域时,提出利用图像修复算法将篡改块匹配问题转化为非局部自相似图像块的最小化问题,复制攻击图像本身的相似信息来恢复篡改区域。实验结果表明,在水印容量为0.015626的情况下,该算法得到嵌入水印后图像的峰值信噪比(peak signal to noise ratio,PSNR)均值超过60 dB,恢复图像的PSNR均值超过40 dB。
基金Project(12GJ6055)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2010FJ4107)supported by Hunan Provincial Science and Technology Department,China
文摘The performances of repaired image depend on the local information in the repaired area and the consistency between the repair directions with structural content.Image repair algorithm with texture information performs well in repairing seriously damaged images,but it has bad performances when the images have the abundant structure information.The dual optimization image repair algorithm based on the linear structure and the optimal texture is proposed.The algorithm uses the double-constraint sparse model to reconstruct the missed information in large area in order to improve the clarity of repaired images.After adopting the preference of Criminisi priority,the image repair algorithm of self-similarity characteristics is proposed to improve the fault and fuzzy distortion phenomena in the repaired image.The results show that the proposed algorithm has more clarity in the image texture and structure and better effectiveness,and the peak signal-to-noise ratio of the repaired images by proposed algorithm is superior to that by other algorithms.
文摘In general there is a digital image with noise, low contrast, blnrred edges and other defects. To effectively enhance the contrast of the image blur to meet the requirements of the subsequent identification and detection. This paper presents a fuzzy adaptive image contrast enhancement algorithm based on gray entropy. This method not only enhances the overall image contrast, but also effectively enrich the target image detail information, and suppress the noise amplification. Meanwhile, the paper proposes an improved K and P parameters image restoration algorithm. The algorithm combines both isotropic and anisotropic diffusion, the use of regional differences in the frequency achieved in the different regions use different iterative equation. Experimental results show that the algorithm with TV model algorithm compared with the same premise of restorative effects, avoiding the staircase effect and better than the TV model repair speed.
