Abstract

In this paper, a high dynamic range (HDR) imaging method based on the stereo vision system is presented. The proposed method uses differently exposed low dynamic range (LDR) images captured from a stereo camera. The stereo LDR images are first converted to initial stereo HDR images using the inverse camera response function estimated from the LDR images. However, due to the limited dynamic range of the stereo LDR camera, the radiance values in under/over-exposed regions of the initial main-view (MV) HDR image can be lost. To restore these radiance values, the proposed stereo matching and hole-filling algorithms are applied to the stereo HDR images. Specifically, the auxiliary-view (AV) HDR image is warped by using the estimated disparity between initial the stereo HDR images and then effective hole-filling is applied to the warped AV HDR image. To reconstruct the final MV HDR, the warped and hole-filled AV HDR image is fused with the initial MV HDR image using the weight map. The experimental results demonstrate objectively and subjectively that the proposed stereo HDR imaging method provides better performance compared to the conventional method.

Highlights

  • Most commercial charge coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) sensors deliver a limited dynamic range (DR) which is usually several orders of magnitude lower than that of a real scene

  • While some approaches enhance the DR by using particular sensors [1,2,3], other high dynamic range (HDR) imaging methods use image processing techniques to generate a high-quality HDR image from low dynamic range (LDR) images captured by low-cost cameras

  • The rectified LDR images are first converted to the initial HDR images, Rim and Ria, by using the proposed inverse camera response function (ICRF) method

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Summary

Introduction

Most commercial charge coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) sensors deliver a limited dynamic range (DR) which is usually several orders of magnitude lower than that of a real scene. While some approaches enhance the DR by using particular sensors [1,2,3], other HDR imaging methods use image processing techniques to generate a high-quality HDR image from low dynamic range (LDR) images captured by low-cost cameras. These HDR imaging methods use multiple LDR images of the same scene captured under different exposures and fuse them into the HDR image [4,5,6,7]. The ghosting artifact can be removed by using special sensors that support spatially varying pixel exposures [12] Such sensors increase the cost of the cameras and reduce the resolution of the resultant HDR image.

Overall Framework
Disparity
Disparity Estimation
Hole-Filling for the matching
Hole-Filling for the Warped AV HDR Image
12. Restoration
Experimental Setup
Evaluation of Performance
14. Input image pairs exposure from the Middlebury database:
For notational the rejection shown in
19.19. Resultant
20. Resultant HDR-VDP2 maps for the Middlebury database and the IIS Jumble
Acknowledgments:
Full Text
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