Image Restoration Using a Combination of Blind and Non-Blind Deconvolution Techniques
Abstract
One of the important implementations in image-processing field is the image restoration. Image restoration deals with the recovery of an original image from a degraded image using a mathematical model of degradation and restoration for image. Image restoration is becoming more and more important in the image-processing field, and it is very important in many applications like medical, satellite and photography. In spite of the various existing solutions available to image restoration, there is always a need for more efficient methods. In this paper, several restoration and deconvolution techniques, experimented and tested, we used both blind and non-blind techniques. Then we propose a combination between blind and non-blind techniques in order to improve the quality of the restored image. Several types of noise are added to the image after it has been blurred. We have tested the behavior of the different filters and techniques in removing each type of noise. The evaluation of the filters behaviors and the conclusion are done based on various metrics like PSNR, MSE, RMSE and IEF.
Keywords
Download Options
Introduction
The restoration of the image is an area that also deals with improving the appearance of an image [1][3]. However, unlike image enhancement, which is subjective, the image restoration is objective in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. In order to obtain a better restoration technique, it is necessary to study and compare the details of the existing restoration filters and then to develop a more powerful filter, which can fulfill the desire to have a cleaner image after removing the noise from it, and achieve a powerful solution for the issues facing image restoration filters. In this paper one image, will be restored, using several restoration techniques, after being degraded by being blurred and noise added. Several types of noise will be used in the degradation process. Noise in the image, is that degradation of an image signal, caused by an external disturbance when the image sent from one place to another place by satellite, wireless, or cable network. There are many types of images noise, in this paper we will use the most common four types: 1- Salt & Pepper noise, which known as shot noise, impulse noise or Spike noise. Its appearance is randomly scattered white or black or both pixel over the image. 2- Gaussian noise which can caused by random fluctuations in the signal; it is modelled by random values added to an image. This noise has a probability density function (pdf) of the normal distribution. It is also known as Gaussian distribution. 3- Speckle noise, it can be modelled by random values multiplied by pixel values of an image. 4- Poisson noise. Individual photon detections can be treated as independent events that follow a random temporal distribution. As a result, photon counting is a classic Poisson process [24][26].In section 2, we will declare image restoration steps; provide a brief description of the blur function, degradation model, and restoration model. In section 3, the restoration techniques are described. In section 4, a proposed method will be illustrate and explained. Section 5, experimental results are shown. Finally section 6, we will give our conclusions.
Conclusion
In our paper we made two kinds of comparison, the first is a comparison of several restoration techniques in restoring an image that has been degraded with a blur function and several kind of noise. We used several criteria to evaluate the performance of each restoration technique: PSNR, RMSE, IEF, and the time of execution of each of the restoration techniques, which has been illustrated in the previous chapter. The Restoration Techniques, overall, the Bilateral filter has the best results among the several types of noise, but in the presence of the Salt & Pepper noise the best result it is that been obtained by the median filter, from this point of view we can state that the nature of the noise effect the efficiency of the deconvolution method. On the other hand, the blind image deconvolution (BID) and the regularized filter has offered the lowest results comparing to the rest restoration techniques. On the other hand, the efficiency of the blind image deconvolution and the non-blind deconvolutions is very high at blur only images. The proposed combination of two restored images has generated an image with a better vision and higher values in PSNR, and lower values in MSE results.