Research Implications of Image Enhancement

The main media for human beings to transfer information are language and images. According to statistics, visual information accounts for 80% of all information received by human beings, so image information is a very important information transfer media and methods. Image transfer system includes image acquisition, image compression, image coding, image storage, image communication, image display of the six parts. In practice, each part may lead to image quality deterioration, so that the information transmitted by the image can not be normally read and recognized. For example, in the process of collecting images due to the lighting environment or object surface reflections and other reasons resulting in the image of the overall uneven illumination, or image acquisition system in the acquisition process due to mechanical equipment can not be avoided to join the acquisition of noise, or the limitations of the image display equipment caused by the image display level sense of reduction or color reduction and so on. Therefore, the study of fast and effective image enhancement algorithms has become one of the key elements to promote the development of the field of image analysis and image understanding.

Image enhancement processing is an important branch of digital image processing. Many of the visual effects of image shooting are poor due to the influence of scene conditions, which requires image enhancement techniques to improve the human visual effect, such as highlighting certain characteristics of the target object in the image, extracting the characteristic parameters of the target object from the digital image and so on, which are conducive to the recognition, tracking and understanding of the target in the image. The main content of image enhancement processing is to highlight the parts of interest in the image and attenuate or remove the unwanted information. This makes the useful information to be strengthened, so as to get a more practical image or convert into a more suitable for human or machine to analyze and process the image. The application areas of image enhancement are also very broad and involve various types of images. For example, in military applications, enhancement of infrared images to extract our interest in enemy targets; in medical applications, enhancement of X-ray images of the patient's brain, chest to determine the exact location of the disease; in space applications, the use of space cameras to send the moon picture enhancement to improve the quality of the image; in agricultural applications, enhancement of remote sensing images to understand the distribution of crops; in transportation applications, foggy weather images to enhance the image to enhance the quality of the image; and the use of remote sensing images to enhance the quality of the image. In traffic applications, foggy weather images are enhanced to strengthen the identification of license plates, road signs and other important information; in digital cameras, enhancement of color images can reduce uneven light, color distortion and other image degradation caused by the phenomenon.

Image engineering is a comprehensive discipline, which has a very wide range of research content and coverage. Since 1996, a review article on the statistical classification of image engineering literature has been published continuously in the Chinese Journal of Image Graphics. According to the main content of each literature will be categorized into five major categories of image processing, image analysis, image understanding, technology application and review, and on this basis of 15 important Chinese journals about image engineering in China, the various types of literature in each journal were statistically and analytically analyzed. The selected journals are: CT Theory and Application Research, Journal of Surveying and Mapping, Journal of Electronic Measurement and Instrumentation, Journal of Electronics, Journal of Electronics and Information, Journal of Computer Science, Pattern Recognition and Artificial Intelligence, Data Acquisition and Processing, Journal of Communication, Signal Processing, Journal of Remote Sensing, Chinese Journal of Biomedical Engineering, Chinese Journal of Somatology and Image Analysis, Chinese Journal of Image Graphics, and Chinese Journal of Image Analysis. Analysis, Chinese Journal of Image Graphics, and Journal of Automation.

From these, we have selected statistics from 2005 to 2009: of the 2,734 academic research and technical application papers published in 112 issues in 2005, 656 papers belonged to the field of image engineering. In 2006, out of 3013 academic studies and technical applications published in 112 issues, 711 documents belonged to the field of image engineering. In 2007, there were 895 papers in the field of image engineering out of 3312 papers published in 118 issues. Among the 3359 academic research and technology application papers published in Issue 120 of 2008, there are 915 papers belonging to the field of image engineering, and among the 3604 academic research and technology application papers published in Issue 134 of 2009, there are 1008 papers belonging to the field of image engineering. These statistics show that both the total number of papers and the total number of selections are growing year by year. The growth in the total number of papers indicates the continuous development of publications, and the increase in the total number of selections indicates the continuous growth of research and application of image engineering. According to the statistics from 1995 to 2009, the total number of articles published on image processing is 2720, accounting for 33.1% of the total number of image engineering; the total number of articles published on image analysis is 2434, accounting for 29.6% of the total number of image engineering; the total number of articles published on image comprehension is 1192, accounting for 14.5% of the total number of image engineering; the total number of articles published on technology application is 1797, accounting for 21.9% of the total number of image engineering; the total number of articles published on review is 21.9% of the total number of image engineering; the total number of articles published on application of technology is 1797, accounting for The total number of published articles on image understanding is 1192, accounting for 14.5% of the total image engineering; the number of published articles on technology application is 1797, accounting for 21.9% of the total image engineering; the number of published articles on review and comment is 74, accounting for 0.9% of the total image engineering, among which the growth rate of the articles on image enhancement technology is especially high. Therefore, image enhancement technology will remain a hot spot in the coming period.

There are many factors that affect the clarity of image quality, uneven outdoor illumination will cause the image gray scale is too concentrated; camera images obtained through digital / analog conversion, line transmission will produce noise pollution, the image quality is inevitably reduced, the lighter the realization of the image accompanied by noise, it is difficult to see the details of the image; the heavier the image is fuzzy and unclear, and even the approximate object appearance of the contours are difficult to see. Therefore, before analyzing and processing the image, it is necessary to improve the image, that is, enhance the image. Image enhancement does not take into account the reasons for the decline in image quality, but only the important features of interest in the image to selectively highlight, while attenuating the unwanted features, the purpose is to improve the intelligibility of the image.

The method of image enhancement is divided into two kinds of null domain method and frequency domain method, the null domain method is to manipulate the pixel points in the image, which is described by the formula as follows:

g(x,y)=f(x,y)*h(x,y)

Which is the f(x,y) original image; h(x,y) is the spatial transformation function; g(x,y) indicates that the image after processing.

Frequency domain method is an indirect method of processing, is first in the frequency domain of the image of the image of the transformed value of the operation, and then change back to the null domain. For example, the image is first Fourier transformed into the frequency domain, then the spectrum of the image is corrected by some kind of filtering, and finally the corrected image is Fourier inverted into the null domain to enhance the image. The process can be depicted in Figure 1.