A Depth Map Generation Algorithm Based on Saliency Detection for 2D to 3D Conversion

Yang, Yana; Hu, Xionglou; Wu, Nengju; Wang, Pengfei; Xu, Dawen; Shen, Rong

doi:10.1007/s13319-017-0138-7

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…6.4 and 0.081 averagely. At the same time, the average SSIM values of the [8] are higher than the proposed system about 0.06.…”

Section: Resultsmentioning

confidence: 77%

“…The results of the depth map are shown in Table 3. It is clear from the Table 3 that the PSNR and VIF values of the proposed system are dominant over the [8] i.e. 6.4 and 0.081 averagely.…”

Section: Resultsmentioning

confidence: 98%

“…Enhanced images do not require dehazing and DFB based enhancement. To check the performance of the proposed system, the depth map results are compared with the depth map results generated by state of the art algorithm [8]. Full reference 2D image evaluation parameters PSNR,SSIM and VIF [47] have been used to evaluate the quality of the depth map.…”

Section: Resultsmentioning

confidence: 99%

“…Yang et al . [8] proposed a method which classify the input image into the three categories i.e. landscape, closeup and the linear perspective image.…”

Section: Related Workmentioning

confidence: 99%

“…Above all, extracting efficient depth information from a 2D image plays an important role in 3D view generation. Numerous methodologies have been proposed to produce depth map [8–11].…”

Section: Introductionmentioning

confidence: 99%

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Degraded image enhancement by image dehazing and Directional Filter Banks using Depth Image based Rendering for future free-view 3D-TV

et al. 2019

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DIBR-3D technology has evolved over the past few years with the demands of consumers increasing in recent times for future free-view 3D videos on their home televisions. The main issue in 3D technology is the lack of 3D content available to watch using the traditional TV systems. Although, some sophisticated devices like stereoscopic cameras have been used to fill the gap between the 3D content demand and 3D content supply. But the content generated through these sophisticated devices can not be displayed on the traditional TV systems, so there needs to be some mechanism which is inline with the traditional TV. Furthermore, the huge collection of existing 2D content should be converted to 3D using depth image-based rendering techniques. This conversion technique can highly contribute in overcoming the shortage problem of the 3D content. This paper presents a novel approach for converting 2D degraded image for DIBR 3D-TV view. This degraded or noisy/blur image is enhanced through image dehazing and Directional Filter Bank (DFB). This enhancement is necessary because of the occlusion effect or hole filling problem that occurs due to imperfect depth map. The enhanced image is then segmented into the foreground image and the background image. After the segmentation, the depth map is generated using image profiles. Moreover, Stereoscopic images are finally produced using the DIBR procedure which is based on the 2D input image and the corresponding depth map. We have verified the results of the proposed approach by comparing the results with the existing state-of-the-art techniques.

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“…6.4 and 0.081 averagely. At the same time, the average SSIM values of the [8] are higher than the proposed system about 0.06.…”

Section: Resultsmentioning

confidence: 77%

“…The results of the depth map are shown in Table 3. It is clear from the Table 3 that the PSNR and VIF values of the proposed system are dominant over the [8] i.e. 6.4 and 0.081 averagely.…”

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

“…Yang et al . [8] proposed a method which classify the input image into the three categories i.e. landscape, closeup and the linear perspective image.…”

Section: Related Workmentioning

confidence: 99%

“…Above all, extracting efficient depth information from a 2D image plays an important role in 3D view generation. Numerous methodologies have been proposed to produce depth map [8–11].…”

Section: Introductionmentioning

confidence: 99%

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