Depth-of-field level estimation method based on single frame image

A frame image and image technology, applied in image analysis, image data processing, calculation, etc., can solve problems such as difficulty in depth estimation, and achieve the effect of increasing complexity

Inactive Publication Date: 2019-01-01
SOUTHEAST UNIV
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AI Technical Summary

Problems solved by technology

It is undoubtedly very difficult to obtain accurate depth estimation from a single image

Method used

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  • Depth-of-field level estimation method based on single frame image
  • Depth-of-field level estimation method based on single frame image
  • Depth-of-field level estimation method based on single frame image

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Embodiment Construction

[0032] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0033] The present invention provides a method for estimating depth levels based on single-frame images, which is a simple and easy-to-operate method for estimating depth of field in daytime scenes, without relying on complex samples and pixel classifiers, and is an end-to-end image processing algorithm. While achieving good estimation results, it can greatly reduce the amount of calculation, and is suitable for running on embedded platforms, drones and other application scenarios. Based on the related theory of dark channel, a simple and easy-to-implement method for image depth level estimation is deduced.

[0034] The farthest visible point detection method based on a single frame image of the present invention is as follows: figure 1 shown, follow the steps below:

[0035] Step 1: Calculate the dark channel image of the image. The dark cha...

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Abstract

The invention discloses a depth-of-field grade estimation method based on a single frame image, which is carried out according to the following steps: step 1, calculating a dark channel image of the image according to the dark channel theory; step 2, calculating that gray scale image of the air transmittance of the image according to the prior theory of the dark channel; step 3, statistic that airtransmissivity map obtained in the step 2 to obtain a gray level histogram; step 4, performing threshold analysis on that gray level histogram to select an appropriate threshold; step 5, applying a threshold value to that transmissivity image to obtain the segmented depth-of-field image. The invention is a simple and easy-to-operate depth-of-field estimation method for daytime scene, which does not need to rely on complex samples and pixel classifiers, and is an end-to-end image processing algorithm. At the same time, it can greatly reduce the amount of computation, which is suitable for embedded platform, UAV and other application scenarios. Based on the theory of dark channel, a simple and easy-to-implement method for estimating the depth of field is derived.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to a depth level estimation method based on a single frame image. Background technique [0002] Depth of field is an important feature of the image captured by the camera, which is of great significance in image processing and video analysis. In video surveillance in the wild, the large distance gap in the image will bring great challenges to smoke detection and other video analysis tasks. Long-distance target recognition requires a more sensitive threshold to ensure the recognition rate, while short-range target recognition requires a wider threshold to reduce the false positive rate. Appropriate depth level estimation can provide important prior knowledge for subsequent recognition and detection. In applications such as forest fire prevention, depth estimation has an important impact on algorithm performance. If the algorithm parameters of different depths o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/80G06T7/50G06K9/46G06K9/62
CPCG06T7/50G06T7/80G06V10/50G06F18/24
Inventor 路小波曹毅超
Owner SOUTHEAST UNIV
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