Dense lightfield reconstruction from multi aperture cameras

Abstract

Plenoptic cameras based on micro lens arrays as well as multi aperture cameras are able to capture a multitude of images with slightly shifted viewpoints. Although the amount of parallax between adjacent views is limited, precautions have to be taken in order to avoid alias when performing direct lightfield rendering. Against this background, we present an approach for the dense reconstruction of a lightfield based on a sparse lightfield acquired from a multi aperture camera with subsequent disparity estimation and depth image based view interpolation. Results show that the approach is suitable for all-in-focus-rendering

Geometry based three-dimensional image processing method for electronic cluster eye

“…As shown in Fig. 13, the proposed method has a better performance than the one gained by method in [59] and by the method in [46], especially for the third and the fourth image, where the words on the slanted plane have less artifact. In addition, the MSE listed in Table 3 also indicates that the proposed method has the minimal reconstructed error among the three methods.…”

“…In this section, the feasibility of the proposed method is tested and the comparisons with the two methods in [59,46] are made in terms of the quality of reconstructed images and the depth map. Actually, the depth estimation method in [59] is replaced by the cost filter technique [15] in this paper. Both the proposed method and the other algorithms for comparison were implemented in MATLAB 2016b.…”

“…If the object is not in the specified distance, the image blurring appears. In [59], Ziegler et al presented an algorithm based on stereo matching and fusion method to obtain the final depth map and complete image. The depth of each channel image was obtained by fusing the results of stereo matching of four neighbouring images.…”

“…Due to the fact that the direction of FOV between adjacent channels has a shift and the width of each image is only 55 pixels, which lead to an inherent parallax between two adjacent channel images and little usefully contextual information for depth matching, respectively, the traditional two-image stereo correspondence algorithms easily cause mismatching. Although the method proposed by Ziegler et al [59] estimates the disparity of one channel image with four neighboring images, essentially, this method is based on two image stereo corresponding algorithm. In order to gather as much information as possible for depth estimation, this study uses the pixels from different images located at similar spatial coordinate to compute the matching cost simultaneously.…”

“…Further, a sharply focused image and a depth map can be gained. Compared with the methods in [30,31,59], G3D has three main advantages:…”