Advanced Multimedia Processing Lab -- Projects -- All Focused Light Field Rendering by Fusion

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Project - All Focused Light Field Rendering by Fusion


·         Team Member

·         Motivation and Goal

·         Approach

·         Results

·         Publications

·         Contact


Team Member


Akira Kubota

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Motivation and Goal

In contrast to traditional geometry based methods, image based rendering (IBR) methods can render a novel view of a scene using a set of pre-acquired images without requiring geometry. However, IBR method requires a large number of images captured with densely arranged cameras (i.e., high sampling density of light field on the camera plane) for rendering a novel view with sufficient quality. If the sampling density is low, the rendered view suffers from aliasing artifacts such as blur and ghost.

In this project, we propose a novel IBR method that enables us to render a novel view with sufficient quality using less number of images compared with that required for non-aliased rendering. Our method is not based on pixel selection or depth estimation. We model the multiple views in a novel form and create all in-focus view by globally performing iterative filtering operations on the multiple views.

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Our approach consists of two steps:

(1)   Rendering multiple views at a given view point using light field rendering (LFR) with different focal plane depths.

(2)   Iterative reconstruction of all in-focus view (a non-aliased view) by fusing the multiple views generated in the step (1).

The step (2) of our approach can reconstruct an all in-focus view directly from the multiple interpolated views without depth map estimation. We model aliasing artifacts as spatially varying filters and the multiple rendered views as a set of linear equations with a combination of textures at the focal depths. We can solve this set of linear equations for the textures by using an iterative reconstruction method and obtain the desired all in-focus view as the sum of the solved textures. This method effectively integrates the focused regions in each view into an all in-focus view with less error. Note that this method does not use any local processing steps such as feature matching, image segmentation and depth estimation.

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We used 81 real images captured with a 9x9 camera array, which are provided from “The Multiview Image Database,” courtesy of the University of Tsukuba, Japan. Image resolution is 480x360 pixels and the distance between cameras is 20 [mm]. The scene contains an object (a doll) in the depth range of 590-800 [mm], which is the target depth range in this experiment. In this experimental condition, the distance between cameras is sparser by about 5 times than that required for non-aliased rendering.

Figure (a) shows the novel views reconstructed by the conventional LFR with the corresponding optimal depth at 5 different view points. In Figure (a), the face of the doll appears in focus, while other regions far from the face appear blurry or ghosted. The conventional LFR algorithm cannot reconstruct all in-focus views at this sampling density. The novel views reconstructed by the proposed method at the same view points are shown in Figure (b). It can be seen that all the regions of the object are reconstructed in focus without visible artifacts. In this reconstruction, we set five focal planes at different depths, and render the novel views using LFR at those depths. Examples of the views are shown in Figure (c), from which the final view at the bottom of Figure (b) is reconstructed. From the top to the bottom in Figure (c), the focal depth is changed from near to far. Although many artifacts occur in the regions that are not in-focus, most of those artifacts cannot be observed in the final views in Figure (b).

 The “Doll” scene (851 KB QuickTime MPEG-4 format)

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Any suggestions or comments are welcome. Please send them to Akira Kubota 

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