Image Enhancement Techniques for Images at Blur Motion and Different Noises

Sahlah Ali

doi:10.33899/csmj.2020.163502

Abstract

Image restoration is an art to improve image quality by disposing of reducing the amount of noise and blurring present in the image. So it is one of the research titles of interest to the researchers. The images get degraded due to environmental conditions and atmospheric difference, it is therefore important to retrieve original images using different algorithms of image processing. There are widespread applications to restore images in our world today. In this research we present a new proposed method to manipulate image using median filter and has also applied inverse filter to image restoration, then combining the proposed method with inverse filter. The proposed method has been evaluated and compared with inverse Filter and with the merger by using different performance with parameters to verify efficiency and performances of these methods. The experimental results on the test image will explain the capabilities of the proposed method to restored image, were the value of the image quality and PSNR (peak Signal to Noise ratio) is better compared with inverse filter, where values of PSNR for Lena Image is 70.3536 and for Girl Image is 71.5410 when using the proposed method of median filter, while values of PSNR when using inverse filter are 62.3225 and 62.6008 respectively when speckle noise.

Highlights

Algorithms of image processing are essentially developed to overcome various problems, some of these include image restoration, image enhancement, image representation, image reconstruction, image preprocessing, image analysis and image data compression[1]
Several criteria in terms of MSE, Peak signal to noise ratio (PSNR) [12], Mean Absolute Error (MAE), RMSE (Root Mean Square Error) and Entropy [3] are presented to assess the quantitative performance of our work, which are shown in tables (1,2,3) and they are given as: PSNR = 10 Log10 [ (d(x,y)max - d(x,y)min)2/ MSE ] (6) Where, MSE symbolizes the Error between the original and the restored images, and is given as: MSE = 1/MN
This paper focuses on restoring the original image with minimum degradation in order to produce the image with high-quality by canceling or reducing the noises and Blurs from the degraded image

Summary

Introduction

Algorithms of image processing are essentially developed to overcome various problems, some of these include image restoration, image enhancement, image representation, image reconstruction, image preprocessing, image analysis and image data compression[1]. You can capture an image by using various digital devices and these may be corrupted due to several reasons, to handle these image restoration techniques must be used to recover original image. The image maybe corrupted due to camera misfocus or motion blurs if there is a proportional motion between camera and the captured image, so that recovering the image is essential for many technological applications [2]. The final filter was combined with the proposed method and obtained the results of the merger, after that, all the results were compared and simulated by using some standard performance, standards such as Entropy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Peak signal to noise ratio (PSNR)

Methods

Discussion

Conclusion