Quick and effective image enhancing method

Abstract

The invention discloses a quick and effective image enhancing method. The quick and effective image enhancing method comprises the following steps of: firstly, obtaining a grey-scale image, then increasing global contrast ratio of the obtained grey-scale image by utilizing a QDHE algorithm with background compensation brightness preservation so as to obtain a grey-scale image with the increased global contrast ratio, increasing the local contrast ratio of the obtained grey-scale image by utilizing an unsharp masking algorithm with noise suppression to obtain a grey-scale image with the increased local contrast ratio, and finally superposing the grey-scale image with the increased global contrast ratio and the grey-scale image with the increased local contrast ratio to output a grey-scale image of an enhanced image.. According to the quick and effective image enhancing method disclosed by the invention, a dark area is compensated when a high-brightness area is prevented from being excessively enhanced, and a noise point is pressed when marginal information is enhanced, and thus the quality of an image is largely improved and can be processed in real time when an image with lower time complexity. The quick and effective image enhancing method disclosed by the invention is widely applied to the field of optical identification.

Description

technical field [0001] The invention relates to image processing technology, in particular to a fast and effective image enhancement method based on the combination of an improved histogram equalization algorithm and an improved unsharp mask algorithm. Background technique [0002] For identifying information in grayscale images, such as recognizing QR code information in two-dimensional barcode images and text information in text images, due to the influence of lighting environment, it is easy to cause the quality of grayscale images to decline, especially when the light is insufficient. When , it will cause the gray value of the bar in the grayscale image (that is, the gray value of the pixel used to represent information) and the empty gray value (that is, the gray value of the pixel used to represent the blank) to be relatively close, In turn, the contrast between the gray value of the bar and the gray value of the space is reduced, and at the same time, a large amount o...

AI Technical Summary

Problems solved by technology

The global enhancement technology uses some conversion functions to process the brightness channel or color channel of the grayscale image, so that the grayscale image reaches the maximum display dynamic range of the display device to obtain more image details. This technology usually uses linear or nonlinear function, histogram equalization, fuzzy contrast intensification and other methods to indirectly improve the contrast of the entire grayscale image, but the improvement of local details is insufficient; local enhancement techniques usually use the edge information and local statistical information in the grayscale image. Enhance the local details of the image. However, based on the optimal transfer function and the average edge gray contrast detection method, the method of improving the contrast by stretching the brightness value is proposed, which improves the contrast and also enhances the noise.

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