HDR image construction from multi-exposed stereo LDR images

Sun, Ning; Mansour, Hassan; Ward, Rabab K.

doi:10.1109/icip.2010.5653371

Cited by 40 publications

(18 citation statements)

References 21 publications

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“…Second, asymmetrical/mixed distortions are realistic in practice. For example, in the case of multiexposure stereo images [45], because of the different exposure levels being used on different views, the amount of noise coming into the left-and right-view image sensors is different. For another example, asymmetric blur distortions and asymmetric blocking artifacts can be found in the case of mixed-resolution coding and asymmetric transform-domain quantization coding, and such distortions could have mixed types when postprocessing techniques (deblocking or blurring) are employed.…”

Section: Subjective Studymentioning

confidence: 99%

Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

Wang

Rehman²,

Zeng

et al. 2015

IEEE Trans. on Image Process.

149

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Objective quality assessment of distorted stereoscopic images is a challenging problem, especially when the distortions in the left and right views are asymmetric. Existing studies suggest that simply averaging the quality of the left and right views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this paper, we first build a database that contains both single-view and symmetrically and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we find that the quality prediction bias of the asymmetrically distorted images could lean toward opposite directions (overestimate or underestimate), depending on the distortion types and levels. Our subjective test also suggests that eye dominance effect does not have strong impact on the visual quality decisions of stereoscopic images. Furthermore, we develop an information content and divisive normalization-based pooling scheme that improves upon structural similarity in estimating the quality of single-view images. Finally, we propose a binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images. Our results show that the proposed model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction of the stereoscopic images.

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Section: Subjective Studymentioning

confidence: 99%

Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

Wang

Rehman²,

Zeng

et al. 2015

IEEE Trans. on Image Process.

149

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show abstract

“…For example, there are a number of methods that exploit stereo camera capturing rigs, where the two cameras are set to capture different exposures [31,156,235,239]. The images require reliable stereo matching in order to align the separated views of the cameras.…”

Section: Multi-camera Methodsmentioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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“…Several approaches have been presented for constructing an HDR image from two differently exposed LDR stereo images by calculating the depth information of the scene [12][13][14][15][16][17]. More recently, Batz et al [18] and Orozco et al [19] proposed interesting approaches for HDR video reconstruction using depth information.…”

Section: Related Workmentioning

confidence: 99%

Backward compatible HDR stereo matching: a hybrid tone-mapping-based framework

Akhavan

Kaufmann

2015

J Image Video Proc.

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Stereo matching under complex circumstances, such as low-textured areas and high dynamic range (HDR) scenes, is an ill-posed problem. In this paper, we introduce a stereo matching approach for real-world HDR scenes which is backward compatible to conventional stereo matchers. For this purpose, (1) we compare and evaluate the tone-mapped disparity maps to find the most suitable tone-mapping approach for the stereo matching purpose. Thereof, (2) we introduce a combining graph-cut based framework for effectively fusing the tone-mapped disparity maps obtained from different tone-mapped input image pairs. And finally, (3) we generate reference ground truth disparity maps for our evaluation using the original HDR images and a customized stereo matching method for HDR inputs. Our experiments show that, combining the most effective features of tone-mapped disparity maps, an improved version of the disparity is achieved. Not only our results reduce the low dynamic range (LDR), conventional disparity errors by the factor of 3, but also outperform the other well-known tone-mapped disparities by providing the closest results to the original HDR disparity maps.

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HDR image construction from multi-exposed stereo LDR images

Cited by 40 publications

References 21 publications

Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Backward compatible HDR stereo matching: a hybrid tone-mapping-based framework

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