Image processing device and image processing method

Abstract

The invention provides an image processing device and an image processing method corresponding to the image processing device. The image processing device comprises an original image storage part, a histogram detection part, a histogram statistic part, a probability density calculation part, a probability density accumulation part, a histogram equalization calculation part which obtains a preprocessing image, a histogram equalization image storage part, a local background calculation part, a luminance threshold value calculation part, a frequency-covering threshold value calculation part, a threshold-identifiable deviation calculation part, a contrast ratio calculation part, a threshold contrast ratio calculation part, a grey-value-correction calculation part and a judgment part, wherein the judgment part judges and corrects the preprocessing image and obtains an optimized image on the basis of the above parts. The image processing device and the image processing method improve the image visual effect and image component definition function based on the human visual characteristics.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to an image processing device and an image processing method for improving the visual effect of images and improving the clarity of image components. Background technique [0002] With the advent of the information age, the application of digital image technology is becoming more and more extensive. In the current technology category, in some environments where the light is extremely strong or weak, image acquisition equipment such as digital video cameras cannot fully achieve the image capture capability of the human eye, so some image enhancement technology equipment is often needed to assist in improving the captured image. The ability to express the details of the image information is more suitable for the observation of the human eye. So far, there have been many image enhancement methods, one of which is to redistribute the gray scale of the image so that it ca...

AI Technical Summary

Problems solved by technology

However, since the histogram grayscale distribution function of histogram equalization is an approximate uniform distribution function, the brightness values ​​obtained by the transformation function are accurate to decimal brightness values, and the final grayscale level is likely to exceed 256 levels. range, which requires replacing it with the nearest integer brightness value, some adjacent values ​​will become the same value during the replacement process, resulting in a "merger effect", resulting in pixel differences in certain areas of the image When the pixel grayscale difference is reduced to a certain extent, it is difficult to be distinguished by the human eye, so that adjacent pixels with different grayscales will form an area of ​​the same grayscale in human vision , some details in the image are "obliterated" by the background

Images