基于稀疏表示模型的图像解码方法

Image Decoding Method Based on Sparse Representation Model

为了更好地提取图像信号的稀疏特性,提出了一种多方向自回归稀疏模型及其重建算法.多方向自回归稀疏模型利用图像局部统计相关和纹理方向实现了图像稀疏表示.在基于变换的编码框架下,以编码端的变换矩阵为观测矩阵,用多方向自回归稀疏模型代替解码端的反变换.图像仿真结果表明,所提出的技术能改善JPEG图像的质量.

To obtain the sparse property of signals better, a mliti-directional adaptive sparse model and recovery algorithm for it in compressive sensing were proposed. The mliti-direetional autoregressive model could use the local statistical correlation and texture directions of image to represent signal sparsely. In a transform based codec framework, the transform matrix was regarded as a measurement matrix. The traditiohal inverse transform in decoder is replaced by the muhidirectional adaptive sparse model. Simulation results over a wide range of images show that the proposed technique can improve the reconstruction quality of JPEG.