Restore blurred and defocused images using a Wiener filter. Implementation on C ++ OpenCV

In the continuation of the article about the restoration of defocused and blurred images I want to share my results with the Wiener filter for restoring real images. OpenCV 3.4 was used as an image processing library. Camera - Nikon D320, Nikon DX AF-S NIKKOR 18-105mm lens, defocusing was done manually, shooting was done without a tripod.

The results of recovering defocused images

During defocusing, the distorting system is well approximated by a cylindrical point spread function (PSF) of radius r.

Cylindrical PSF

Below are the results of the restoration of three real defocused images of the same object (book page). The survey was carried out without a tripod from a distance of approximately 50 cm. The degree of defocusing of the lens manually increased from frame to frame. The Wiener filter parameters r and the signal-to-noise ratio (SNR) were selected manually in such a way as to ensure the best visual quality of the recovery. To compensate for edge effects, a gradual decrease in the brightness of the image at the edges is made.

Picture A

Result of image restoration A. r = 53, SNR = 5200

Picture B

Result of image restoration B. r = 66, SNR = 4400

Image C

Result of image restoration C. r = 102, SNR = 7100

It is seen that even with a significant defocusing, the readability of the text is almost
completely restored.

Results of restoring blurred license plate images

Image blur occurs when the camera and the object move relative to each other during exposure. Consider only the case when the subject is moving linearly relative to a fixed camera. In this case, the distorting system is well approximated by the PSF in the form of a segment, which is directed along the motion of the object. Such PSF is defined by two parameters: L length and THETA blur angle.

PRT with linear lubrication

Below is a distorted image of two cars, obtained with insufficiently short exposure, which led to a noticeable blurring.

Distorted image of two cars

Below are the results of the restoration of the numbers of both cars using the Wiener filter. The values ​​of the parameters L, THETA and SNR were selected in such a way as to ensure the best visual quality of the car number recovery.

The result of the restoration of the light car number. L = 78, THETA = 15, SNR = 300

The result of the restoration of the dark car number. L = 125, THETA = 0, SNR = 700

It can be seen that even with significant lubrication it is possible to restore the readability of
car numbers .

The filtering algorithm is implemented in C ++ OpenCV as a console application.
Source codes can be found at the links below.
https : //


  1. RC Gonzalez, RE Woods. Digital image fundamentals. 1987
  2. I.S. Gruzman, V.S. Kirichuk, V.P. Kosykh, G.I. Peretyagin, A.A. Spector. Digital image processing in information systems. 2000

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