超完备稀疏表示的图像超分辨率重构方法

为改善单帧退化图像的分辨率，提出一种基于超完备字典稀疏表示的图像超分辨率重构方法。该方法的核心是构建信号自适应的超完备字典对及计算图像关于对应字典的稀疏表示。为降低在训练过程中构建超完备字典对的复杂性，采用学习低分辨率字典而数值计算高分辨率字典的方法，待超分辨图像应用正则正交匹配追踪的稀疏表示算法求解关于字典的稀疏表示，并联合高分辨率字典实现超分辨率重构。实验表明，该方法与其他类似算法相比，字典训练和超分辨测试的速度都有显著提高，实验图像的峰值信噪比改善3.3 dB，框架相似性提高0.09。本方法可应用于单帧模糊图像的高倍率的超分辨率重构，有效地提高了图像的分辨率水平。

Image super-resolution reconstruction algorithm using over-complete sparse representation

In order to enhance the resolution of single degraded images, a method of super-resolution reconstruction is proposed via over-complete sparse representation. The core of the super-resolution problem is to construct over-complete dictionary pairs and represent sparsely signals with respect to associated dictionary. For reducing the complexity of building dictionary pairs in training phase, the low-resolution dictionary is learned from patches but the high-resolution counterpart is numerically calculated using known sparse coefficients. In testing stage, a sparse representation of the low-resolution image over its dictionary is solved with a regularized orthogonal matching pursuit algorithm; thereby super-resolution reconstruction is realized by jointing an optimizing high-resolution dictionary. Experimental results demonstrate that, compared with other similar techniques, the peak signal-to-noise ratio (PSNR) gain of super resolved images is 3.3 dB, and the improvement of structural similarity (SSIM) is 0.09. Especially, both training efficiency and testing speed of this proposed algorithm have dramatically improvement. This approach can be applied over the high-ratio super-resolution reconstruction of single-frame blurred images, hence the resolution level of given images is effectively improved.