结合深度线索和几何结构的稀疏光场密集重建

Sparse light fields dense reconstruction combining depth cues and geometric structures

光场一次成像可以同时获得空间和角度的四维信息。现有方法进行角度超分辨率重建时多用于小基线场景的光场图像,在处理大基线场景重建时存在模糊等现象,同时在光场重建过程中遮挡区域重建效果差、长距离的空间关系难以捕获。针对上述问题,提出一种结合深度线索和几何结构的稀疏光场密集重建方法。该方法采用空间金字塔池化提取多尺度特征,更好地保留了图像的纹理细节和高频信息;通过在深度估计模块的部分引入空洞卷积并进行密集连接,扩大了感受野,提高了大基线场景深度估计的精度;利用视图细化模块对图像进行优化处理,在保留视差结构的同时重建了遮挡区域。实验结果表明,本文方法较好地解决了大基线场景光场重建问题,在光场大基线场景数据集上超越了其他算法,PSNR提高了2 dB,SSIM提高了0.018,重建图像的质量均优于现有的算法。

The four-dimensional information of space and angle can be obtained by one-time imaging of light field.The existing methods are mostly used for the light field image of small baseline scene in the angular super-resolution reconstruction,and there are some phenomena such as blur when reconstructing the large baseline scenes.At the same time,the reconstruction effect of the occlusion area is poor in the process of light field reconstruction,and the long-distance spatial relationship is difficult to capture.To solve this problem,a sparse light field intensive reconstruction method combining depth clues and geometric structure is proposed.This method uses spatial pyramid pool to extract multi-scale features,which can preserve the texture details and high frequency information of images better.By introducing void convolution and dense connection in the depth estimation module,the receptive field is expanded and the accuracy of depth estimation of large baseline scene is improved.The view refinement module is used to optimize the image and reconstruct the occlusion area while preserving the parallax structure.Experimental results show that the proposed method solves the problem of largebaseline scene optical field reconstruction well,and exceeds other algorithms in the large-baseline scene data set,with PSNR increased by 2 dB and SSIM increased by 0.018.The quality of reconstructed images is superior to the existing algorithms.