联合非均匀采样和压缩感知的图像压缩算法

摘要

为了提高图像重构精度,改善纹理区域视觉效果,本文将压缩感知理论与图像压缩相结合,并提出了一种新的采样方法:在编码端对图像高频部分边缘点进行密集采样,对非边缘部分进行随机抽样,取代了传统压缩感知理论中直接使用测量矩阵获得低维观测值的过程.在解码端利用采样点位置信息构造块测量矩阵,使用光滑l0范数(Smoothed l0,SL0)重构算法实现重叠块重构,最终将其与图像低频部分下采样点插值放大结果合并实现高精度重构.实验结果表明:本文算法不仅可以提高整幅图像和纹理区域的重构精度,而且在低采样率或图像尺寸较小的情况下,算法效率也有明显提升.%In order to improve the reconstruction precision of the image and the visual presentation of the texture areas, this paper applied compressed sensing theory to image compression, and proposed a new kind of sampling methods: it sampled the edge of the high frequency part of the image densely and the non-edge part randomly in the encoder, instead of using the measurement matrix to obtain the lower-dimensional observation directly in the traditional compressed sensing theory. In the decoder, this paper used the position of the sample-points to structure the block measurement matrix, realizing a overlap-block image reconstruction using smoothed l0 reconstruction algorithm, combined the result with the interpolation amplification of the down-sampled points of the low frequency part of the image realizing a high precision image reconstruction. The experimental result shows that the proposed algorithm can not only improve the reconstruction precision both of the whole image and the texture areas, but also increase the efficiency obviously under the low sampling rate or the small size image.