Image quality enhancement of computational integral imaging reconstruction for partially occluded objects using binary weighting mask on occlusion areas

Abstract

This paper presents an image quality enhancement of computational integral imaging reconstruction (CIIR) method by using a binary weighting mask on occlusion areas in elemental images. The proposed method utilizes a block-matching algorithm to estimate the occlusion areas in elemental images. Then, a binary weighting mask generated from the estimated occlusion area is applied to our CIIR method. This minimizes the overlapping effect of occluding objects in the reconstructed plane images and thus improves visual quality dramatically. To show the usefulness of our proposed scheme, we conduct several experiments and present the results. The experimental results indicate that our method is superior to the existing methods.

Computational Integral Imaging Reconstruction of a Partially Occluded Three-Dimensional Object Using an Image Inpainting Technique

“…Computational integral imaging (CII) is able to capture and reconstruct partially occluded 3D objects for 3D visualization and recognition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. CII consists of the pickup process and the subsequent computational integral imaging reconstruction (CIIR) process.…”

“…In CIIR, the elemental images are digitally processed using a computer, by which 3D images can be easily reconstructed at a desired reconstruction plane. This CII approach is very useful in the visualization and recognition of 3D objects which are partially occluded by interposed objects [13][14][15][16][17][18][19][20].…”

“…Studies to address this problem [14][15][16][17][18] have been based on removing the occlusion from the recorded elemental images to obtain plane images of good visual quality. However, after the occluding object has been removed, corresponding holes remain in the elemental images.…”

Neighboring Elemental Image Exemplar Based Inpainting for Computational Integral Imaging Reconstruction with Partial Occlusion

“…This property enables the CII to reconstruct partially occluded 3-D objects for 3-D visualization and recognition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Several occlusion removal methods have been studied in [15][16][17][18][19] to solve the problem of the degraded resolution of the computationally reconstructed 3-D image, which occurs due to the partial occlusion.…”

Phase-only hologram generation based on integral imaging and its enhancement in depth resolution

“…The elemental image set contains various perspective images, which are captured at different positions. From this property, the elemental image set is applied for other purposes such as depth map calculation [6,7] , occluded region reconstruction [7] , and depth-slice image generation [8,9] . We use the elemental image set for the purpose of generating depth-slice images, which is the basic information for multi-plane phase-only hologram [4] .…”