提出了一种基于小波域局部高斯模型的图像超分辨率算法。小波域局部高斯模型采用单一的高斯函数刻画子带系数的局部概率分布，由于该模型具有很好的局部自适应性，可以较好地反映图像的局部结构信息，因此以此作为自然图像的先验模型，将图像超分辨率问题转化为小波域约束优化问题，并用共轭梯度法对其进行求解。实验结果表明，基于小波域局部高斯模型的图像超分辨率算法较好地再现了图像的各种边缘信息，重构出的高分辨率图像在信噪比和视觉效果方面都有较明显的提高。

An image super-resolution algorithm based on wavelet-domain local gaussian model is proposed. Wavelet-domain local gaussian model approximates the local probability distribution of the wavelet coefficients with a single gaussian function. Because the model adaptively characterizes the local statistics of real-world images, the algorithm presented in this paper specifies the prior distribution of the real-world image through it and converts the image super-resolution problem to a constrained optimization one which can be solved with the conjugate gradient method. Experimental results show that the algorithm properly retrieves various kinds of edges and the PNSR and subjective visual effect of the reconstructed images are improved significantly.