In the process of actually acquiring and transmitting images, image distortion often occurs, and the obtained image is somewhat different from the original image. These are all because external noise is added to the image, so before processing the collected image, the image needs to be preprocessed, that is, the noise image is processed.
Line filtering to smooth out noisy images. Traditional noise reduction methods include median filtering, mean filtering, Wiener filtering, etc. These methods have obvious limitations for non-stationary process signals.
The multi-image averaging method is often used in the video image of the camera to reduce the noise caused by the photoelectric camera tube or CCD device of the TV camera. This is to continuously take multiple images of the same scene and digitize them.
The principle of the average method of multiple imagesThe multi-image averaging method is a method of collecting the same scene repeatedly M times and adding the average value to eliminate noise. The image imaging model [4] can be described as:
It can be seen that the variance of the multi-image averaging method is M1 of the original image. As M increases, the noise suppression effect becomes stronger. The flow chart of this algorithm is shown in Figure 1.
There is often a certain amount of noise in the images collected by the camera. This noise is generally uncorrelated in the spatial domain and is an additive noise. For an image collected by a camera, it can be expressed as a composition of noise-free image and additive noise, that is:
g (x, y) = f (x, y) + η (x, y) (1)
Among them: g (x, y) is the collected image, f (x, y) is the noise-free image, and η (x, y) is the noise.
The process of denoising is the process of approximating f (x, y) from the known g (x, y).
For multiple images taken in the same scene, fi (x, y) is the same, and ηi (x, y) is random and not related to each other. The average value of k images in the same scene can be expressed as follows
g¯ (x, y) = 1K∑i = 1K [fi (x, y) + ηi (x, y)] = f (x, y) + 1K∑i = 1Kηi (x, y) (2)
Since the noise is random and uncorrelated, the average image expectation can be obtained
E {g¯ (x, y)} = f (x, y) (3)
Variance of the average imageσ2g¯ (x, y) = 1Kσ2η (x, y) (4)
which is
σg¯ (x, y) = 1K−−√ση (x, y) (5)
From equation (3), we can find that the average value of multiple images in the same scene is expected to be a noise-free image, but there will be some disturbances, and the standard deviation of these disturbances (5) determines the intensity of the noise. The essence of our image denoising is to reduce the standard deviation in the spatial domain. From equation (5), it is not difficult to find that by increasing the value of K, that is, increasing the number of average images, the noise can be reduced.
But at the same time, we can find: σâˆ1K√, ∂σ∂K = −12K3√, with the increase of K value, the change of σ becomes smaller and smaller. When denoising by the average method, the effect of increasing the number of images is only Is very small.
Purpose
1. Verify that multiple images can be denoised on average in the same scene.
2. As the number of images increases, the image noise changes less and less.
data set
179 photos of the same scene were collected in a short time.
Below is one of the pictures
Its local details:
It can be found that there is more noise on the image.
programming
The processing procedure of the program is:
* Read image
* Averaging
* Display image
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