Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms

Shim, Inwook; Oh, Tae-Hyun; Lee, Joon-Young; Choi, Jinwook; Choi, Dong‐Geol; Kweon, In So

doi:10.1109/tcsvt.2018.2846292

Cited by 33 publications

(17 citation statements)

References 51 publications

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“…First, the image exposure quality evaluation model proposed in this paper is verified; then, the performance of the cubic spline interpolation curve in improving the exposure accuracy of the imaging system and improving the image exposure quality is verified. In the experiment, the camera’s default method (CDM), average gray level method (AGLM) [ 12 ], one-dimensional entropy method (ODEM) [ 15 ], entropy weighted gradient method (EWGM) [ 21 ], gradient-based method (GBM) [ 19 ], etc. are used for comparison, which proves the superiority of the proposed method.…”

Section: Verification and Analysismentioning

confidence: 99%

“…They calculated the derivative of the gradient square and the photometric response function, and measured the change of the gradient with the exposure time to determine the optimal exposure time. Shim et al also used the gradient information in the image to get the appropriate exposure time [ 18 , 19 ]. The author defines an information metric based on the size of the gradient at each pixel, and simulates exposure changes by applying different gamma corrections to the original image to find the gamma value that maximizes the gradient information, and then adjust the exposure time according to the gamma value.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

Gao

Zheng

et al. 2021

Sensors

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The improper setting of exposure time for the space camera will cause serious image quality degradation (overexposure or underexposure) in the imaging process. In order to solve the problem of insufficient utilization of the camera’s dynamic range to obtain high-quality original images, an automatic exposure method for plane array remote sensing images based on two-dimensional entropy is proposed. First, a two-dimensional entropy-based image exposure quality evaluation model is proposed. The two-dimensional entropy matrix of the image is partitioned to distinguish the saturated areas (region of overexposure and underexposure) and the unsaturated areas (region of propitious exposure) from the original image. The ratio of the saturated area is used as an evaluating indicator of image exposure quality, which is more sensitive to the brightness, edges, information volume, and signal-to-noise ratio of the image. Then, the cubic spline interpolation method is applied to fit the exposure quality curve to efficiently improve the camera’s exposure accuracy. A series of experiments have been carried out for different targets in different environments using the existing imaging system to verify the superiority and robustness of the proposed method. Compared with the conventional automatic exposure method, the signal-to-noise ratio of the image obtained by the proposed algorithm is increased by at least 1.6730 dB, and the number of saturated pixels is reduced to at least 2.568%. The method is significant to improve the on-orbit autonomous operating capability and on-orbit application efficiency of space camera.

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Section: Verification and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

Gao

Zheng

et al. 2021

Sensors

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“…They proposed some AEC algorithms for computer vision applications specifically. For example, Shim [8] and Zhang [9] claimed that the performance of a semantic comprehension algorithm is highly reliant on the intensity of local gradients. Hence the target of the AEC algorithm should be providing the image with the most outstanding value of gradient amplitude.…”

Section: Related Workmentioning

confidence: 99%

Auto Complementary Exposure Control for High Dynamic Range Video Capturing

Han

Jia

Song³

et al. 2021

IEEE Access

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Recent trends in consumer electronics have led to a proliferation of high dynamic range (HDR) video acquisition and transmission studies. High-end video capturing systems increase the bit depth of each pixel to adapt to the high-contrast ambient light. However, the extended bit depth is pathetically meager compared with the far wider dynamic range of the incident light. What's more, incompatibility issues for the subsequent compression and transmission system may be arisen along with the increased bit depth. In this paper, we propose an auto complementary exposure (ACE) algorithm for optimal exposure parameter estimation under Siamese Trigger (ST) mode to maximize the entropy of the output HDR image. With the proposed algorithm, neither the sensor nor other related hardware needs to be modified. Only the auto-exposure algorithm in the firmware needs to be updated. In the proposed ACE algorithm, every two consecutive images are treated as Siamese twins, and the exposure parameters for the twin images are calculated simultaneously. The target of the ACE algorithm is not to maximize the entropy of each image but to maximize that of the fused image. Compared to existing auto exposure or bracketed fusing algorithms, the proposed algorithm has three significant advantages. Firstly, no extra bit-depth is needed to embrace the details of the scene. Thus the image transmission system is not affected. Secondly, only two raw images are necessary for one output fused HDR image. The computation load is relatively low. Thirdly, the proposed method can fit video recording, which has a broader application prospect than HDR image capturing. Experiment results on a self-built database and publicly accessible database demonstrate that the algorithm works well under various types of scenarios. Furthermore, the experiment results on the real-time system show that the captured video image reveals many details under high dynamic scenes.

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“…This problem is well studied mathematically and numeric approaches have shown success in reaching a desired exposure quickly and accurately [23] [21].…”

Section: B Exposure Controlmentioning

confidence: 99%

Dedicated Exposure Control for Remote Photoplethysmography

et al. 2020

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This paper aims to show that control of exposure time during video capture will improve the accuracy of remote photoplethysmography (rPPG). We propose a purpose specific exposure control algorithm for use in heart rate estimation via rPPG applicable for any controllable camera. Our novel algorithm works by selecting exposure that acheives maximum Signal-to-Noise Ratio (SNR) before distortion will occur. We performed experiments to test the accuracy of non-contact PPG extracted simultaneously from two identical cameras positioned together but with different exposure time controls. Our purpose specific algorithm in camera A controlled exposure time to maximise rPPG SNR ratio while camera B remained set at one of a range of values. Exposure time set by our novel algorithm out-performed camera B with a lower mean absolute error relative to a standard pulse oximeter. A significant improvement to heart rate estimation performance using a research camera can be made with specific control of exposure time. The improvements in performance demonstrated here are an important step in taking rPPG out of a lab environment and into less controlled circumstances such clinical settings and emergency rescue scenarios.

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Gradient-Based Camera Exposure Control for Outdoor Mobile Platforms

Cited by 33 publications

References 51 publications

An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

Auto Complementary Exposure Control for High Dynamic Range Video Capturing

Dedicated Exposure Control for Remote Photoplethysmography

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