Fully automatic real-time digital image processing enhancement system

Abstract

In the system, the input module is used for collecting the inputted digital image; the collected digital images is inputted into the image dissection module; the image dissection module dissects image into a light illumination image L and a reflection image R, both are respectively inputted into the light illumination processing module and the reflection processing module; the light illumination processing module makes non-linear correction for the light illumination image L and output an corrected light illumination image L'; the reflection module makes the partial noise reduction process for the reflection image R corresponding to the dark area of the inputted image and outputs a noise-reduced reflection image R'; the image combining and outputting module combines L' and R' and then displays the combined image on the output device.

Description

technical field [0001] The present invention relates to an image processing system in the field of digital image technology, in particular to a fully automatic real-time digital image processing enhancement system. Background technique [0002] With the popularization of digital cameras, digital images play an increasingly important role in production and life. Especially in production automation, digital images play an important role in object recognition and object tracking. However, due to the defects of imaging technology itself, the quality of digital images is affected, and the application of digital images is limited. [0003] The dynamic range of brightness in real life is very large, mainly affected by ambient lighting, and the brightness in direct sunlight and shadows often differ by several orders of magnitude. In contrast, the dynamic range of digital cameras is much smaller, and the longest 8-bit image depth can only represent 256 brightness levels. Under dif...

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Problems solved by technology

The camera does not have this self-adjusting function, so in the case of poor lighting conditions (too dark or too bright), the object of interest is difficult to identify on the image, and the quality of the image is also reduced a lot

[0004] The general processing methods for this problem are often gray level equalization or gamma correction. However, these two processing methods are global processing methods and ignore local information. improved, but the details of local images may be lost

[0006] The disadvantage of the above system is that although the system can improve the lighting effect of the input image, the noise in the input image is also enhanced while the details of the image are enhanced. Therefore, for the input image that originally contains more noise, the output The quality of the image may be worse than the input image

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