由于终端设备硬件条件的限制或者传输过程中带宽的限制，通常获取到的图像和视频分辨率质量较低，不能满足实际应用的需求。为了克服这一困难，研究人员提出超分辨率技术，利用已知的低分辨率图像恢复出高分辨率图像，已广泛应用于卫星遥感、数字娱乐、视频监控等多个领域。

超分辨率需要充分挖掘图像包含的信息，提升图像的空间分辨率，但是目前很多超分辨率算法都没有将图像的非局部相似性考虑进去，造成信息的浪费，算法性能存在进一步提升的空间。为了解决上述问题，本论文重点研究了基于压缩感知和非局部相似的图像超分辨率算法，以及基于非局部相似的视频序列超分辨率算法。

针对图像超分辨率研究，本论文提出一种联合图像稀疏性和非局部相似性的图像超分辨率重构方法：1）给出一种基于测量域的字典分类方法，将样本图像块在测量域分为平滑、纹理、边缘块，使用分类后的样本块分别训练对应类别的字典，对每个待重构图像块选取与它最相近的字典进行重构，提高字典的稀疏表示能力；2）重构时，为解决仅利用外部库学习先验知识造成重构结果细节不真实的问题，充分利用图像自身的非局部相似特征，在整幅图像内搜索待重构图像块的相似块，将图像块的稀疏性和非局部相似性同时作为重构过程的约束条件，联合恢复出细节真实的高分辨率图像。

针对视频序列超分辨率研究，本论文提出一种基于非局部相似的视频超分辨率重构算法：1）给出基于自适应平滑策略的分层光流法对多帧视频序列进行配准，主要包括图像预处理降低外部光照影响、平坦区域自适应平滑提高估计准确性、每层图像使用上一层矢量结果变形解决大位移精度较差问题；2）利用MAP（最大后验概率）理论实现多帧图像的融合，给出一种基于梯度边缘增强的非局部模型，对于正则化项计算几何距离影响部分时，利用图像的非局部相似性计算两个像素点的相似权重，并引入梯度算子加强边缘部分对结果的影响，保证重构图像具有良好边缘效果。

最后，实验结果表明，本论文提出的图像/视频超分辨率算法在重构图像的主观质量和客观质量方面均有良好的性能，可应用于手机娱乐、视频监控等实际场景中。

Due to the limitations of physical resolution of the terminal devices or the bandwidth in the transmission process, it is difficult to get the high-resolution images that meet the basic requirement of applications. To overcome this difficulty, super-resolution is proposed to reconstruct a high-resolution image based on the existing low-resolution images, which has been widely used in many fields, such as satellite remote sensing, digital entertainment, video surveillance and so on.

Image spatial resolution has been improved by digging deeply into the information embedded in image, however, many super-resolution algorithms ignore the non-local similarity of image, resulting in the waste of priori information, there still exists room to improve the performance. To resolve the problems above, this thesis mainly focuses on image super-resolution based on compressed sensing and similarity constraint, and multi-frame video super-resolution based on non-local similarity.

For image super-resolution algorithm, an image super-resolution based on compressed sensing and similarity constraint is proposed in this thesis: 1) A dictionary classification method based on measurement domain is proposed, image blocks are divided into smooth, texture and edge parts, the corresponding dictionaries have been trained using the classified image blocks, which can improve the representation of dictionaries. 2) In reconstruction, to solve the problem that learning prior knowledge only from external training sets causing the false detail result, the image non-local similarity is fully used, all similarity image blocks are searched in the whole image, and the sparsity and similarity of blocks are both took as constraints in reconstruction process to improve the authenticity of the high resolution image.

For video super-resolution algorithm, a multi-frame video super-resolution based on non-local similarity is proposed in this thesis: 1) A hierarchical optical flow based on adaptive smoothing strategy is proposed to estimate motion vector, including image preprocessing to reduce the effects of external lighting, adaptive smoothing strategy in flat area to improve estimation accuracy, warp each layer image using result vector of upper layer to solve the problem of poor accuracy in large displacement; 2) The multi-frame images fusion is realized by MAP theory, and a non-local model based on gradient edge enhancement is presented, for the regularization term, the similar weight of two pixels is calculated by image non-local similarity, and the gradient operator is introduced to enhance the effect of the edge part, to ensure the reconstructed image has a good edge effect.

Finally, the experimental results show that the proposed super-resolution algorithms in this thesis have good performance in both subjective and objective aspects, and can be applied to practical scenes such as mobile entertainment and video surveillance.