针对目前图像重建方法去噪效果不佳,导致重建后图像分辨率较低的问题,提出基于单层小波变换的视觉传感图像超分辨率重建方法。建立低分辨率和高分辨率两种识别空间,分别计算含有噪声干扰区域、正常区域以及信道噪声参数三者间的欧式距...针对目前图像重建方法去噪效果不佳,导致重建后图像分辨率较低的问题,提出基于单层小波变换的视觉传感图像超分辨率重建方法。建立低分辨率和高分辨率两种识别空间,分别计算含有噪声干扰区域、正常区域以及信道噪声参数三者间的欧式距离。利用二维平滑函数定义单层小波变换,有效去除视觉传感图像中的噪声,根据多尺度特性对图像中处于边缘微值的分辨率进行具体检测。对所有高分辨率点实行编码,再将编码后的图像系数按照分辨率的高低顺序整理为集合,输出图像完成重建。仿真实验证明,所提方法重建后图像清晰度较高,且结构相似性(Structural Similarity Index Measurement, SSIM)与峰值信噪比(Peak Signal to Noise Ratio, PSNR)的值均高于对比方法,最高值分别为0.95 dB与34.57 dB,说明所提方法的重建效果较好。展开更多
In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the ...In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.展开更多
文摘针对目前图像重建方法去噪效果不佳,导致重建后图像分辨率较低的问题,提出基于单层小波变换的视觉传感图像超分辨率重建方法。建立低分辨率和高分辨率两种识别空间,分别计算含有噪声干扰区域、正常区域以及信道噪声参数三者间的欧式距离。利用二维平滑函数定义单层小波变换,有效去除视觉传感图像中的噪声,根据多尺度特性对图像中处于边缘微值的分辨率进行具体检测。对所有高分辨率点实行编码,再将编码后的图像系数按照分辨率的高低顺序整理为集合,输出图像完成重建。仿真实验证明,所提方法重建后图像清晰度较高,且结构相似性(Structural Similarity Index Measurement, SSIM)与峰值信噪比(Peak Signal to Noise Ratio, PSNR)的值均高于对比方法,最高值分别为0.95 dB与34.57 dB,说明所提方法的重建效果较好。
文摘In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.
