Extremely dark image visual quality improving method, system and device and medium

Abstract

The invention discloses an extremely dark image visual quality improvement method, system and device, and a medium, and the method comprises the steps: extracting a bright channel graph of an obtainedextremely dark image, and calculating the incident light of the image based on the bright channel graph; converting the RGB space of the extremely dark image into an HSV space, and extracting a V channel graph of the HSV space; creating a total variation model, and inputting the image incident light and the V channel diagram into the total variation model to obtain a denoised image; and in the HSV space, enhancing the contrast of the denoised image, and converting the processed denoised image into the RGB space to complete the improvement of the visual quality of the extremely dark image. Themethod not only can effectively improve the contrast of the extremely dark image, but also has a better enhancement effect on other dark images of different degrees, and can also effectively suppressnoise in the image illuminance improving process.

Description

technical field [0001] The present disclosure relates to the field of image processing, and in particular to a method, system, device and medium for improving the visual quality of extremely dark images. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] At night, or in low-brightness or extremely dark scenes, due to the limitation of sensitivity of traditional imaging devices, it is difficult to maintain the image color of natural scenes. For imaging of darker or partially dark scenes, image acquisition can be increased Device exposure time to obtain high brightness images. However, longer exposure times can lead to blurred images, and images acquired under extremely dark conditions often have a lot of noise. In the traditional image enhancement method, the histogram matching method is generally used to restore the color information, but ...

AI Technical Summary

Problems solved by technology

[0003] At night, or in low-brightness or extremely dark scenes, traditional imaging devices are limited by sensitivity, and it is difficult to maintain the image color of natural scenes. For imaging of darker or partially dark scenes, image acquisition can be increased Device exposure time to obtain high brightness images

However, longer exposure times lead to blurred images, and images acquired under extremely dark conditions often contain a lot of noise

In the traditional image enhancement method, the method of histogram matching is generally used to restore the color information, but although this method can effectively improve the image contrast, there will also be an over-enhancement phenomenon, resulting in the problem of image color distortion; and in extremely dark conditions Acquired images often have a lot of noise, and traditional image enhancement methods are not effective in suppressing noise in extremely dark images

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