基于L0范数稀疏表达的图像盲超分辨率重建

Blind Image Super-resolution Reconstruction Based on L0 Norm Sparse Representation

由于大部分超分辨率图像重建方法都是建立在图像的点扩散函数为已知或假设点扩散函数为高斯模糊核的条件下,但真实的低分辨率图像中的点扩散函数并不是高斯函数,而是由随机的相机抖动造成的。为了提高重建的超分辨率图像质量并使其更接近真实场景,提出了一种基于L0范数稀疏表达的图像盲超分辨率重建方法。首先利用了基于L0范数的梯度最小化方法估计出超分辨率图像中的点扩散函数,再通过点扩散函数的估计在超分辨率重建的过程中有效地去除图片的模糊效应,最后利用反向传播算法,使重建的超分辨率图像更接近真实。通过实验结果表明,提出的方法相对于双三次插值法和基于多字典学习的图像超分辨率重建算法可以得到更清晰的重建效果,峰值信噪比和平均结构相似度均有提高,最后在真实图片重建测试效果中也得到了更好的验证。

Most of super-resolution reconstruction methods are based on the condition that the point spread function is known or is Gaussian fuzzy kernel. However, the real point spread function of low resolution image, caused by random camera shake, is not Gaussian function. To improve the quality of the reconstructed super-resolution image and make it close to the real scene, a blind image super-resolution method is proposed based on LO norm sparse representation. First, the point spread function in original images is estimated by using the gradient minimization method based on LO norm, which is then used for removing the fuzzy effect of image in the process of super resolution reconstruction. At last, the backward propagation algorithm is used to make the reconstruction super-resolution image close to the reality. The experimental results show that：compared with bicubic interpolation and multi-dictionary learning method, the proposed method can get more clear reconstruction effect, and the PSNR and MSSIM are improved. The method has been validated by reconstruction test of real pictures.