Is large improvement in efficiency of impulsive noise removal in color images still possible?

Lukasz Malinski,Bogdan Smolka,Krystian Radlak,Yiming Tang

doi:10.1371/journal.pone.0253117

Abstract

The substantial improvement in the efficiency of switching filters, intended for the removal of impulsive noise within color images is described. Numerous noisy pixel detection and replacement techniques are evaluated, where the filtering performance for color images and subsequent results are assessed using statistical reasoning. Denoising efficiency for the applied detection and interpolation techniques are assessed when the location of corrupted pixels are identified by noisy pixel detection algorithms and also in the scenario when they are already known. The results show that improvement in objective quality measures can be achieved by using more robust detection techniques, combined with novel methods of corrupted pixel restoration. A significant increase in the image denoising performance is achieved for both pixel detection and interpolation, surpassing current filtering methods especially via the application of a convolutional network. The interpolation techniques used in the image inpainting methods also significantly increased the efficiency of impulsive noise removal.

Highlights

Different types of impulsive noise decrease the quality of digital, color images and may be caused through transmission errors, electromagnetic disturbances, ageing of the storage material, sensors imperfections, and flawed memory regions [1,2,3,4]
The paper starts with the question: Is large improvement in efficiency of impulsive noise suppression in color images still possible? After extensive testing, performed using numerous impulsive noise detection and interpolation algorithms and thorough analysis of the results, there is no doubt that the answer is positive
The occurrence of false positive results lead to a decrease in image quality

Summary

Introduction

Different types of impulsive noise decrease the quality of digital, color images and may be caused through transmission errors, electromagnetic disturbances, ageing of the storage material, sensors imperfections, and flawed memory regions [1,2,3,4]. Impulsive noise can be introduced to images deliberately, due to the fact that deep neural networks are vulnerable to adversarial examples [5]. In one-pixel attacks, altering only few pixels in an original image is enough to fool a deep neural network [6, 7]. Filtering algorithms dedicated to the suppression of impulsive disturbances in color images and considered as defensive methods against adversarial attacks, have attracted considerable interest among many researchers [8,9,10,11,12,13,14,15,16,17]. Intensive development of the methods used for noise reduction in digital images has been observed [18]. Doubts have appeared as to whether these new methods are able to significantly increase

Objectives

Results

Conclusion