Abstract

Motion blur in images is usually modeled as the convolution of a point spread function (PSF) and the original image represented as pixel intensities. The knife-edge function can be used to model various types of motion-blurs, and hence it allows for the construction of a PSF and accurate estimation of the degradation function without knowledge of the specific degradation model. This paper addresses the problem of image restoration using a knife-edge function and optimal window Wiener filtering. In the proposed method, we first calculate the motion-blur parameters and construct the optimal window. Then, we use the detected knife-edge function to obtain the system degradation function. Finally, we perform Wiener filtering to obtain the restored image. Experiments show that the restored image has improved resolution and contrast parameters with clear details and no discernible ringing effects.

Highlights

  • Camera movements might result in motion blur in captured images

  • The blurred image is usually modeled as a convolution between the original image and a known point spread function (PSF)

  • We present an image restoration technique based on the knife-edge function and optimal window Wiener filtering

Read more

Summary

Introduction

The blurred image is usually modeled as a convolution between the original image and a known point spread function (PSF). In [4], an adaptive restoration method to adaptively correct retinal images is proposed This is performed by using deconvolution to remove the residual wave-front aberrations and provide an improvement over the Wiener filter with respect to the quality of restoration. We present an image restoration technique based on the knife-edge function and optimal window Wiener filtering. We use the Prewitt edge detection operator and autocorrelation function to calculate the direction and scale of the motion-blur. G(x,y) represents the blurred image, f(x,y) refers to the original image, h(x,y) is the PSF or degradation function, and n(x,y) is random noise caused by the camera sensor. While performing a Fourier transform of the motion-blurred image g(x,y)

PSFV À 1
Extend the edge to obtain the extension image of
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call