针对现有基于稀疏编码的单幅图像超分辨率重建算法易导致重建图像中出现不正确几何结构的现象, 提出一种字典非相关性约束和稀疏系数非局部自相似性约束结合的稀疏编码方法。为解决引入这种自相似性约束造成的重建图像边缘过度平滑、模糊的问题, 提出了基于平滑层和纹理层的双层重建框架。该方法运用一种全局非零梯度数目约束重建模型重建平滑层; 通过提出的稀疏编码方法重建高分辨率纹理图像。最后, 利用一个全局和局部优化模型进一步提升重建图像的质量。实验结果表明, 与一些具有代表性的重建方法相比, 该方法得到的峰值信噪比(PSNR)和结构相似度(SSIM)平均值分别提高了0.798 7~3.242 4 dB和0.018 6~0.083 5, 不仅主观视觉效果上取得了明显的改进, 鲁棒性得到增强, 而且重建出了更加准确的结构和边缘, 取得了更好的重建效果。

As existing sparse coding methods for single-image easily lead to incorrect geometrical structures in reconstructed images, a sparse coding method combining the incoherence constraint of dictionary and the nonlocal self-similarity constraint of sparse coefficient was proposed . Meanwhile, a double layer reconstruction scheme based a smooth layer (SL) and a texture layer (TL) was presented to overcome the over-smooth edges and blurring problem of the reconstructed images because of introducing the nonlocal self-similarity constraint. The method uses a global non-zero gradient constraint SR model to reconstruct a High Resolution Smooth Image (HRSI), and takes the proposed sparse coding method to recover the HR Texture Image (HRTI). Finally, a global-local constraint optimized model were proposed to improve the quality of the final output image. Experiments indicates that the average values of Peak Signal to Noise(PSNR) and the structural similarity (SSIM) have increased 0.798 7 dB-3.242 4 dB and 0.018 6-0.083 5 as compared with those of some recent representative algorithms。The results demonstrate that the method not only improves the subjective vision obviously, enhances the robustness, but also reconstructs more accurate structures and edges, and receives better reconstruct images.