基于形状自适应非局部回归和非局部梯度正则的深度图像超分辨

针对深度图像分辨率低、深度不连续性模糊问题，提出一种基于形状自适应非局部回归和非局部梯度正则的深度图像超分辨方法。为了探究深度图像非局部相似块之间的相关性，提出了形状自适应的非局部回归。该方法对每个像素点提取其形状自适应块，并根据形状自适应块构建目标像素的相似像素组；然后针对相似像素组中的每个像素，结合同场景的高分辨率彩色图像获得非局部权重，从而构建非局部回归先验。为了保持深度图像的边缘信息，对图像梯度的非局部性进行探究。不同于总变分（TV）正则化对所有像素点梯度的零均值拉普拉斯分布假设，该方法利用深度图像梯度的非局部相似性，用非局部块估计特定像素点的梯度均值，并用学习到的均值来拟合各像素点的梯度分布。实验结果表明，相较于基于边缘不一致性评价模型（EIEM），所提方法在Middlebury数据集上的2倍和4倍上采样率的平均绝对值差（MAD）分别下降了41.1%和40.8%。

Depth image super-resolution based on shape-adaptive non-local regression and non-local gradient regularization

To deal with the low resolution of depth images and blurring depth discontinuities， a depth image super-resolution method based on shape-adaptive non-local regression and non-local gradient regularization was proposed. To explore the correlation between non-local similar patches of depth image， a shape-adaptive non-local regression method was proposed. The shape-adaptive self-similarity patch was extracted for each pixel， and a similar pixel group for the target pixel was constructed according to its shape-adaptive patch. Then for each pixel in the similar pixel group， a non-local weight was obtained with the assistant of the high-resolution color image of the same scene， thereby constructing the non-local regression prior. To maintain the edge information of the depth image， the non-locality of the gradient of the depth image was explored. Different from the Total Variation （TV） regularization which assumed that all pixels obeyed Laplacian distribution with zero mean value， through non-local similarity of the depth image， the gradient mean value of specific pixel was estimated by non-local patches， and the gradient distribution of each pixel was fit by using the learned mean value. Experimental results show that compared with Edge Inconsistency Evaluation Model （EIEM） on Middlebury datasets， the proposed method decreases Mean Absolute Difference （MAD） of 41.1% and 40.8% respectively.