HDR scene detection and capturing strategy

Vallikumari, V.; RaviKiran, B.; Raju, K. Srinivasa

doi:10.1109/indcon.2011.6139400

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…According to B.Ravi Kiran et al [6] [7], the bisection method is used to find the exposure values as the root. The bisection method is the basic numerical analysis method, which is used to find the roots for the continuous functions.…”

Section: Bisection Methodsmentioning

confidence: 99%

False Position based Auto Exposure Algorithm for Properly Exposing the Leather Samples in the Leather Industries

RaviKiran¹,

Krishna²,

Basha³

et al. 2013

IJCA

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Section: Bisection Methodsmentioning

confidence: 99%

False Position based Auto Exposure Algorithm for Properly Exposing the Leather Samples in the Leather Industries

RaviKiran¹,

Krishna²,

Basha³

et al. 2013

IJCA

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“…Similar works that consider the estimation distribution of pixel values as the analysis objects can be found in [3] and [4]. Meanwhile, ValliKumari et al [8] proposed an algorithm to detect HDR scene and to suggest optimal exposure sets for a given scene. In HDR detection, they differentiated the uniform black / white reflectance areas with the underexposed / overexposed regions, by assuming that the image intensity changes almost linearly to the exposure time of the image.…”

Section: Related Workmentioning

confidence: 99%

“…Existing works mainly focus on DSLR cameras [1] - [5] and surveillance video cameras [6]- [8]. Little work has focused on mobile cameras.…”

Section: Introductionmentioning

confidence: 99%

An effective dynamic exposure determination method for high dynamic range photography on iOS devices

Liu

Ercan

Qiang

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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High dynamic range (HDR) images are highly desirable and widely applied in our daily life. However, most conventional digital cameras are only able to capture low dynamic range (LDR) images. To produce HDR images, a popular and effective method is to merge multiple LDR images with different exposure settings. To achieve high quality HDR images, determining the exposure settings when capturing the multiple LDR images is of great importance. Though many researchers have paid attention to the exposure determination issue for HDR imaging, most of them have focused on DSLR cameras and surveillance video cameras. Meanwhile, mobile devices nowadays have increasing use but few studies in literature focus on them. Since cameras of mobile devices have some features different from DSLR cameras and surveillance video cameras, many existing exposure determination methods may not be directly applied on them. To fill this research gap, this paper proposes a dynamic exposure determination method for HDR photography on mobile devices, in particular on iOS mobile devices. In the proposed method, three LDR images are captured on iOS devices, which are then merged to generate an HDR result image that can cover more details of the target scene. Considering the time and memory limits of mobile devices, the proposed method analyzes the histogram of the target scene thumbnail to determine the three exposure settings with suitable exposure gaps. Based on our proposed method, we have developed an iOS HDR Camera App. The experimental results demonstrate that the HDR result images of this App are capable to contain more details in both bright area and dark area than those of Apple's default HDR Camera App on iPhone 5s for real-world target scenes.

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