Elsevier

Optik

Volume 245, November 2021, 167709
Optik

Original research article
Restoration and quality improvement of distorted tribal artworks using Particle Swarm Optimization (PSO) technique along with nonlinear filtering

https://doi.org/10.1016/j.ijleo.2021.167709Get rights and content

Abstract

Cultural artworks that are not properly cared for, degrades over time. However modern image processing techniques proves to be a boon for such artworks. Various techniques have been developed time to time for different aspects of image processing like noise removal, improving brightness and image quality, etc. One of such techniques is PSO (Particle Swarm Optimization) that can be used for image restoration. In this paper a combination of median filter and PSO has been used for the quality improvement of the distorted tribal artworks and the results are compared with few HE (Histogram Equalization) techniques like the CLAHE, BBHE and DSIHE. The comparison is done on the basis of various image quality assessment parameters, namely PSNR, AMBE, NMSE, CPP and MSE.

Introduction

Art has the power to influence people and bring people closer. Art may vary from culture to culture yet it holds the key to bring communities closer. These artworks are storehouse of information about various cultures across the globe. Though in modern times, such cultural significant artworks fail to get much attention or recognition and are usually not properly preserved. These can be preserved through digitisation and those artworks which have lost its radiance over time can be reconstructed or restored using modern technology. In this paper, one such tribal artworks of India have been used as the sample images. The aim of this work is to restore and improve the quality of these artworks that are degraded over time, PSO (Particle Swarm Optimization) technique have been used in this paper. PSO was introduced by Kennedy and Eberhart [1] which was inspired by the motion of a flock of birds. Various such optimization techniques have been proposed over the years, some of which include Ant Colony Optimization (ACO) [2], [3], [4], Differential Evolution (DE) [5], Bacterial Foraging Optimization (BFO) [6], [7], [8], [9], Artificial Bee Colony (ABC) [10], [11], [12], Glow-worm Swarm Optimization (GSO) [13], [14], Bat Algorithm (BA) [15], [16] and more. The two most famous from the above mentioned ones are ACO and PSO. These optimization algorithms are used in different problems related optimization in numerous fields [17], [18], [19]. Considering the image enhancement as an optimization problem allows us to use these techniques for image enhancement as well [20], [21], [22], [23]. To improve the quality of image, a parameterised transformation function [23] is used in this paper, in which the values of the parameters are optimised by PSO technique with the help of an objective function. In this paper, a combination of median Filter and PSO is used for improving the image quality of old and faded colour images. MATLAB software has been used to develop the program. The results obtained are compared with that of other techniques like CLAHE, BBHE and DSIHE [24], [25], [26].

Section snippets

Median filter

It is an example of nonlinear filter i.e., output of median filter is a nonlinear function of its input. Linear filter can’t provide edge preservation, while the nonlinear filter can. The median filter replaces the pixel value by calculating the median of the neighbouring pixels. The neighbouring pixels window size is selected by the user. The activity of a median filter can be represented by the following mathematical equation [27],ux,y=Median[vxm,yn,(m,n)w]Here w represents the size of the

Absolute Mean Brightness Error (AMBE)

It represents the change in absolute mean value of the original and filtered image [36]. This value indicates the brightness preservation rate during the filtering process. Smaller AMBE value is desired because it signifies better brightness preservation.AMBE=MeanIOMeanIrwhere, IO represents original image and Ir is the restored image.

Peak to Signal Noise Ratio (PSNR)

It is a ratio between the maximum signal power to the distorting noise power and is represented in dB [37]. High PSNR value indicates better image filtering. If

Results and discussions

The necessary program for median filtering followed by PSO is developed in MATLAB and is tested on various distorted colour images, results of one such image is presented in this paper. The result obtained from the proposed method is compared with different histogram equalization techniques namely, CLAHE, BBHE and DSIHE. All the mentioned methods’ performance is compared on the basis of image quality parameters like PSNR, AMBE, NMSE, MSE and CPP. Fig. 2(a) shows the distorted and degraded input

Conclusion

A PSO based image restoration method is presented in this paper which pre-processes the image using median filter to remove noise and to give better results. Results are compared with other techniques like CLAHE, BBHE and DSIHE. On comparison, it is found that the method involving PSO gives the best results. Noises of the distorted images are removed using nonlinear median filter. It works well in preserving edges of the input images as well. The corresponding program is developed using MATLAB

Declaration of Competing Interest

The authors certify that there is no conflict of interest with any other.

Acknowledgement

The authors would like to acknowledge Prof. R.C. Jha, Dr. R.K. Sarkar, Dr. S. Karmakar and Birla Institute of Technology, Mesra for the support provided to carry out this work. This work is financially supported by Indian National Science Academy (INSA).

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