Image Restoration Quality Measurement using Noise Filters

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 2

Year of Publication : 2023

Authors : Tushar Debnath, Surajit Paul, Kumar Amitabh

10.14445/23488549/IJECE-V10I2P101

How to Cite?

Tushar Debnath, Surajit Paul, Kumar Amitabh, "Image Restoration Quality Measurement using Noise Filters," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 2, pp. 1-5, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I2P101

Abstract:

The aim of this project is to implement different features of image restoration. We consider a vectorised picture and take pictures of it with our mobile for two configurations, the first lit by natural light, the second without the light. Those photos apply different transformations on the original image as rotation, change of the scale, integration of unwanted environment in the picture, etc. We will see a method to restore those photos and compare them to the original image. The comparison will be made by using two indicators: the Peak Signal Noise Ratio (PSNR), measuring the quality of reconstruction of the image, and the Structural Similarity Index (SSIM), evaluating the similarity between pixels. Finally, the complementary analysis will be performed to increase the restoration quality between the pictures, like using a noise reduction filter or function to increase the correspondence between histograms.

Keywords:

Histogram equalization, Noise reduction, Peak signal to Noise ratio, Sharpening, Structural Similarity Index.

References:

[1] Mark R. Banham, and Aggelos K Katsaggelos, “Digital Image Restoration” IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24- 41, 1997, Crossref, https://doi.org/10.1109/79.581363
[2] J.A. Goyette et al., “Improving Autoradiograph Resolution Using Image Restoration Techniques,” IEEE Engineering and Medicine Biology, pp. 571-574, 1994.
[3] V. Raval, and L. Gagnani, “Introduction to Image Restoration and Comparison of Various Methods of Image Restoration,” International Journal of Advanced Research in Computer Engineering & Technology, vo.1, no. 1, pp. 134-136, 2012.
[4] Bahadir Kursat Gunturk, and Xin Li, Image Restoration Fundamentals and Advances, CRC Press Taylor & Francis Group, 2013.
[5] Jens Breckling, The Analysis of Directional Time Series: Applications to Wind Speed and Direction, Lecture Notes in Statistics, Germany: Springer, vol. 61, no. 1, pp. 200-220, 1989. 
[6] Rujie Yin et al., “A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets,” SIAM Journal on Imaging Sciences, vol. 10, no. 2, pp. 711–750, 2017. Crossref, https://doi.org/10.1137/16M1091447
[7] Weisheng Dong et al., “Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture,” International Journal of Computer Vision, vol. 114, pp. 217–232, 2015. Crossref, https://doi.org/10.1007/s11263-015-0808-y
[8] Kai Zhang et al., “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for image denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142–3155, 2017. Crossref, https://doi.org/10.1109/TIP.2017.2662206
[9] Madri Thakur, and Shilpa Datar, “Image Restoration Based on Deconvolution by Richardson Lucy Algorithm,” International Journal of Engineering Trends and Technology, vol. 14, no.4, pp. 161–165, 2014.
[10] Anamika Maurya, and Rajinder Tiwari, “A Novel Method of Image Restoration by using Different Types of Filtering Techniques,” International Journal of Engineering Science and Innovative Technology, vol. 3, no. 4, 2014.
[11] Zhang Jian Min, and Admore Gota, “Analysis and Comparison on Image Restoration Algorithms using MATLAB,” International Journal of Engineering Research & Technology, vol. 2, no. 12, pp. 1350–1360, 2013.
[12] Mona Aoud et al., “Image Restoration Based on Morphological Operations,” International Journal of Computer Science, Engineering and Information Technology, vol. 4, no. 3, pp. 9–21, 2014.
[13] Ajay Kumar Boyat, and Brijendra Kumar Joshi, “A Review Paper: Noise Models In Digital Image Processing,” Signal & Image Processing: An International Journal, vol.6, no.2, 2015. Crossref, https://doi.org/10.5121/sipij.2015.6206
[14] Rinku Kalotra, and Sh. Anil Sagar, “A Review: A Novel Algorithm for Blurred Image Restoration in the field of Medical Imaging,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no.6, 2014.
[15] Dhananjay K. Theckedath, Digital Image Processing (Using MATLAB Codes), 2nd Edition, 2013.
[16] Amandeep Kaur, and Vinay Chopra, “A Comparative Study and Analysis of Image Restoration Techniques Using Different Images Formats,” International Journal for Science and Emerging Technologies with Latest Trends, vol. 2, no. 1, pp. 7-14, 2012.
[17] Rohit Verma, and Jahid Ali, “A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 10, 2013.
[18] Ajay Kumar Boyat, and Brijendra Kumar Joshi, “A Review Paper: Noise Models In Digital Image Processing,” Signal & Image Processing: An International Journal, vol.6, no.2, 2015. Crossref, https://doi.org/10.48550/arXiv.1505.03489
[19] Ajay Boyat, and Brijendra Kumar Joshi, “Image Denoising using Wavelet Transform and Median Filtering‟, IEEE Nirma University International Conference on Engineering,” pp. 1-6, 2013. Crossref, https://doi.org/10.1109/NUiCONE.2013.6780128
[20] R.C.Gonzales, and R.E.Woods, Digital Image Processing, 2-nd Edition, prentice Hall, 2002.
[21] Priyanka Kamboj1 and Versha Rani, “A Brief Study of Various Noise Models and Filtering Techniques,” Journal of Global Research in Computer Science, vol. 4, no. 4, 2013.
[22] D. Maheswari, and V. Radha, “Noise Removal in Compound Image Using Median Filter,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 2, no. 4, pp. 1359-1362, 2010.
[23] [Online]. Available: http://en.wikipedia.org/wiki/Signal_to_noise_ratio_(imaging)
[24] Dipalee A. Kolte, Maruti B. Limkar, and Sanjay M. Hundiwale, “Character recognition from deblurred motion distorted Vehicle image using Neural Network,” SSRG International Journal of Electronics and Communication Engineering, vol. 1, no. 4, 2014. Crossref, https://doi.org/10.14445/23488549/IJECE-V1I4P101
[25] Nikhil D. Chauhan, Naman Gandhi, Khushbu Joshi and Reena Patel, "Image Denoising and Compression using Wavelate," SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 4, pp. 6-9, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I4P102
[26] Manohar Koli, and Balaji Sriramulu, “Literature Survey on Impulse Noise Reduction,” Signal & Image Processing: An International Journal, vol. 4, no. 5, pp. 75-95, 2013. Crossref, https://doi.org/10.5121/sipij.2013.4506
[27] Jaakko Astola, and Pauli Kuosmanen, Fundamentals of Nonlinear Digital Filtering, CRC Press, Boca Raton. 1997.
[28] Pragati Agrawal, and Jayendra Singh Verma, “A Survey of Linear and Non-Linear Filters for Noise Reduction,” International Journal of Advance Research in Computer Science and Management Studies, vol. 1, no. 3, pp. 18-25, 2013.
[29] Lexing Xie, and Shahram Ebadollahi, “Digital Image Processing,” Department of Electrical Engineering, Image Restoration, 2009. [Online]. Available: http://www.ee.columbia.edu/~xlx/ee4830/
[30] Ruchika Chandel, and Gaurav Gupta, “Image Filtering Algorithms and Techniques: A Review,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 10, pp. 198-202, 2013.