基于自相似性与多任务高斯过程回归的单帧图像超分辨率重建

在单帧图像超分辨率问题中,基于高斯过程回归的超分辨率算法没有挖掘相似图像片间的关联关系或者无差别地用相似图像片来扩充训练集合,都会导致重建的高分辨率图像中存在明显的噪声和伪影。对此提出了一种基于多任务高斯过程回归的超分辨率算法。该算法通过引入多任务学习思想,将输入的低分辨率图像进行分片处理,把每一个图像片的超分辨率过程视为一个任务。在对相似任务建模的过程中,通过最优化求解的参数集合来体现任务间的共性及差异,从而使模型的泛化能力和预测精度得以提高,在重建高分辨率图像清晰锐利的同时,噪声和伪影受到明显抑制。用常见的测试图像以及公开的图像测试集合进行的大量试验表明该算法在主观评价和客观评价两个方面均优于同类型算法及当前经典算法,峰值信噪比较其它常见超分辨率算法可提高约0.5dB。

Single image super-resolution reconstruction algorithm based on image self-similarity and multi-task Gaussian process regression

In the domain of single image super-resolution, algorithms based on Gaussian process regression neither exploit the association relationships among similar patches, nor do they discriminate between these patches with similar properties to augment the volume of the training set, which leads to obvious noise and artifacts in reconstructed high-resolution images. To overcome this problem, a new super-resolution algorithm based on multi-task Gaussian process regression is proposed. This algorithm introduces the idea of multi-task learning to partition the input low-resolution image into overlapped patches and considers the super-resolution process of each patch as a task. In the process of modeling similar tasks, the parameter set obtained by optimal solving for representing the commonness and difference gives generalization ability, improves prediction accuracy improved, makes the reconstructed high-resolution image clear and sharp, and suppresses noise and artifacts significantly. A large number of experiments to process common testing images and a public image test set subjectively and objectively demonstrate that this algorithm is superior to similar state of the art algorithms, and the peak signal to noise ratio is approximately 0.5 dB higher than that of other common super-resolution algorithms.