Noise in high dynamic range imaging

Bell, André A.; Seiler, Claude; Kaftan, Jens N.; Aach, Til

doi:10.1109/icip.2008.4711816

Cited by 16 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The uniform weighting (derived to be optimal in [1]) achieves the lowest SNR, since reliable and unreliable measurements contribute equally. The Kirk-Andersen weighting [8] properly accounts for temporal noise sources (except DCSN), but lacks the spatial ones, hence obtains the optimal SNR minus the bias error.…”

Section: Discussionmentioning

confidence: 99%

Optimal HDR reconstruction with linear digital cameras

Granados

Ajdin²,

Wand³

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

146

168

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Optimal HDR reconstruction with linear digital cameras

Granados

Ajdin²,

Wand³

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

146

168

View full text Add to dashboard Cite

show abstract

“…In HDR imaging, the weighted average of LDRIs for generating the HDR radiance map has the effect that reduces the noise in LDRIs [1,[12][13][14]. In previous work, the weighting functions as w(·) in Eq.…”

Section: Case 2: High-sensitivity Settingmentioning

confidence: 98%

“…For performance comparison of noise reduction in HDR imaging, four existing methods are considered: Debevec and Malik's method [1], Akyuz and Reinhard's method [12], Mitsunaga and Nayar's method [13], and Bell et al's method [14]. Also we compare visual quality of HDRIs of existing and proposed noise reduction methods.…”

Section: Noise Reductionmentioning

confidence: 99%

“…In previous work, the weighting functions as w(·) in Eq. (13) used in computing the weighted average of LDRIs were proposed mainly for reducing the noise [1,14]. The weighted average is similar to smoothing the temporally neighboring pixels in video processing.…”

Section: Case 2: High-sensitivity Settingmentioning

confidence: 99%

“…Bell et al [14] used the uniform weighting function that shows the good performance in terms of the SNR. However, they did not consider the reliability of the pixel values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Noise reduction in high dynamic range images

Min

Park

Chang

2011

SIViP

View full text Add to dashboard Cite

Multiple images with different exposures are used to produce a high dynamic range (HDR) image. Sometimes high-sensitivity setting is needed for capturing images in low light condition as in an indoor room. However, current digital cameras do not produce a high-quality HDR image when noise occurs in low light condition or high-sensitivity setting. In this paper, we propose a noise reduction method in generating HDR images using a set of low dynamic range (LDR) images with different exposures, where ghost artifacts are effectively removed by image registration and local motion information. In high-sensitivity setting, motion information is used in generating a HDR image. We analyze the characteristics of the proposed method and compare the performance of the proposed and existing HDR image generation methods, in which Reinhard et al.'s global tone mapping method is used for displaying the final HDR images. Experiments with several sets of test LDR images with different exposures show that the proposed method gives better performance than existing methods in terms of visual quality and computation time.

show abstract