Noise Reduction Method Based on ccd Image Using Inter-Frame Correlation

Abstract

The invention discloses a CCD-image-based method for reducing noise by using frame-to-frame correlation. The method comprises the following steps: obtaining relative displacement of images of two adjacent frames through an image registration method, determining a folding area of frames of the two adjacent frames through the displacement, then carrying out frame-to-frame accumulation filtering on the folding area, applying an intra-frame filtering algorithm to non-folding areas, wherein the algorithm is also capable of adjusting the number of folding frames in a self-adaptive manner and has a multi-stage adjustable function. The innovation point of the invention is characterized by firstly carrying out image registration, and then selecting different intra-frame filtering algorithm according to a registration result; the signal to noise ratio of video images is increased; meanwhile, the noise of motion images can be well reduced.

Description

technical field [0001] The invention relates to a denoising processing technology of a CCD video image, in particular to a method for denoising based on a CCD image by utilizing inter-frame correlation. Background technique [0002] At present, all kinds of imaging devices have been and are being widely used in the fields of life, astronomy, medical treatment, industry and military, and have an increasingly profound impact on people's lives, especially the rapid development of charge in medicine and military in recent years. Coupled device (CCD) imaging and infrared sensor imaging technology. Compared with traditional imaging devices, CCD not only has the advantages of small size, light weight, high sensitivity, wide dynamic range, etc., but also can achieve high spatial resolution, and can respond to radiation from ultraviolet to infrared by choosing different materials. spectrum, which greatly enhances its range of applications. Due to the structural characteristics of t...

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Problems solved by technology

However, these methods also have their own technical shortcomings: 1. The value of each pixel after the mean value filter is replaced by the average gray value of the pixels in the neighborhood, which often brings negative effects of blurred edges and details. 2. The adaptive Vera filter method is to use the local variance of the image to adjust the output of the filter, and its ultimate goal is to make the restored image f^(x,y) and the original f(x,y) uniform square error e 2 =E[(f^(x,y)-f(x,y)) 2 ] minimum

The filtering effect of this method is better than the mean filtering effect, which is very useful for retaining the edges and other high-frequency parts of the image, but the amount of calculation is large; 3. The median filtering is to use the median of the pixel grayscale in the neighborhood (including the original pixel value) Value instead of the value of the pixel, it can protect the edge of the image while removing noise, but it will produce blurred details; 4. Inter-frame mean filtering is to average the relative positions of adjacent frames, this method can effectively suppress Noise can improve image clarity, but it will cause blurring and loss of moving objects; 5. The non-local filtering algorithm proposed in recent years and the three-dimensional block matching filtering method based on non-local mean improvement are currently considered to be the best method for denoising. But the algorithm is complex and difficult to implement in hardware

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