Low-Light Auto-Focus Enhancement for Digital and Cell-Phone Camera Image Pipelines

Gamadia, Mark; Kehtarnavaz, Nasser; Roberts-Hoffman, K.

doi:10.1109/tce.2007.381682

Cited by 34 publications

(21 citation statements)

References 5 publications

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“…This filters out a lot of noise such that it will not contribute to the focus measure. Other publications (38,39) also discussed the effectiveness of the GFM under low light conditions. Another FMF that has been reported to be effective in reducing noise under low light condition is the frequency selective weighted median (FSWM).…”

Section: Gfm_menmentioning

confidence: 99%

Autofocus System and Evaluation Methodologies: A Literature Review

2018

Sensors and Materials

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The autofocus (AF) system has gained popularity in over 40 years. Embedded in a camera, it can bring the best focused image to the viewer within a few seconds, which frees users from focusing manually. An AF system usually consists of a motor, a lens, and the processing and control units. Within years of development, the AF system has become mature in terms of both technology and market, and many fast and accurate AF systems have been introduced and widely installed in compact cameras and digital single lens reflex (DSLR) cameras. On the other hand, the market for thermal infrared cameras has been increasingly growing in recent years owing to their decreasing cost and wide use by both civilians and the military. The AF system in those cameras adopted similar but more complex mechanisms. This article serves as a literature review of the state of the art of the AF system in both visible light digital cameras and thermal infrared cameras.

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Section: Gfm_menmentioning

confidence: 99%

Autofocus System and Evaluation Methodologies: A Literature Review

2018

Sensors and Materials

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“…(3) is computed by averaging the entropy of membership values in a local neighborhood, which can reduce the effect of noise. Specifically, compared to the wavelet-based method [7,8], which is computation expensive in decomposing the image into different frequency bands and computation of transform, the proposed image definition measure based on fuzzy entropy is more efficient and applicable in practical image measurement system. Different from the LSF method [9] based on line detection, which is effective only for images with lines and sensitive to noise in edge line detection, the proposed image definition measure by averaging the fuzzy entropies in a local neighborhood not only can be used in various types of images but also can reduce the effects of environmental factors such as lens magnification, lighting conditions, and noises.…”

Section: Step 2 Image Definition Evaluationmentioning

confidence: 99%

“…A. Akiyama et al proposed a definition evaluation function based on Daubechies wavelet transform, which sets four weightings according to the decomposed frequency bands and was used in uncooled infrared camera [6]. To solve the problems of low image contrast ratio and flat definition evaluation curve under low light, M. Gamadia et al adopted an image enhancement method to increase the contrast of the image and then designed a corresponding focusing evaluation function to measure the image contrast [7]. Makkapati presented an improved wavelet-based image auto-focusing method for blood smears in microscope [8].…”

Section: Introductionmentioning

confidence: 99%

An image auto-focusing algorithm for industrial image measurement

Liu

Yang

2016

EURASIP J. Adv. Signal Process.

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Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one of the widely used methods for this task because of its fast response, convenience, and intelligence. In general, the image-based auto-focusing algorithm often consists of two important steps which are the image definition evaluation and the search strategy. In this paper, we have developed an image auto-focusing algorithm for industrial image measurement. First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification. Second, a combined search method is proposed to combine the multi-scale global search and fine-level curve fitting method, which can avoid the disturbance of the local peaks and obtain the best image focus. The proposed image auto-focusing algorithm has the advantages of high focusing accuracy, high repeatability and stability under the variations of lens magnification, and light intensity index, which make it applicable for the industrial image measurement. Experimental results and comparisons on the practical industrial image measurement system have been presented to show the effectiveness and superiority of the proposed algorithm.

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“…Shen and Chen [18] use a focus measure involving the coefficients of the image in the discrete cosine transform domain to achieve a higher discrimination ratio between out-of-focus and in-focus images. Gamadia et al [5,6] show that a focus measure based on a Gaussian smoothing step, a contrast enhancement step, and a derivative step, is very effective in low-light situations. In our low-light experiments, we adopt a simplified, but still very competitive, version of the proposal of Gamadia et al, where we omit the contrast enhancement step and perform Gaussian smoothing and taking the derivative all in one step.…”

Section: Related Workmentioning

confidence: 99%