Total views : 216

Performance Assessment of Reclamation Methods for various Distorted Images


  • University Institute of Information Technology, Himachal Pradesh University, Shimla - 171005, Himachal Pradesh, India


Objectives: Performance Assessment of Reclamation Methods aims to analyse the various performance parameters of reclamation techniques for a distorted image. Due to rapid modernization and advancement of technology in last decades, the importance and need of reclamation of image is rapidly increasing and hence need for better way to reclaim an image in best way possible is to be determined. Methods/Statistical Analysis: An approach has been made in this paper to examine that which reclamation method is best for which kind of degraded image (type of noise introduced to image). Basic approach is to find out how filters can be used for restoring or reclaiming an image in such a way that the reclaimed image's maximum quality is achieved, because image processing is one of the major feature in Artificial Intelligence, which has emerged as huge and powerful field in technology. Findings: By comparing original image's quality and reclaimed image's quality we can determine which reclamation method suits for which kind of noise and hence the reclamation of image will get easier. Application/Improvements: This paper helps selecting the most preferable and efficient reclamation method in restoring degraded images.


Image Degradation and Eruption, Image Filtering, Image Processing, Mean Structural Similarity Index, Performance Assessment of Reclamation Methods.

Full Text:

 |  (PDF views: 195)


  • Eskicioglu AM, Fisher PS. Image quality measures and their performance. IEEE Trans Communication. 1995 Dec; 43:2959–65.
  • Sngulagi P. Performance Analysis of Effective Image Restoration Techniques at Different Noises. Indian J Sci Res. 2015; 11(1):009–16. ISSN: 2250-0138.
  • Campisi P, Karen E. Blind image deconvolution: theory and applications. CRC PRESS Boca Raton: London New York Washington, D.C.
  • Thakral B, Ahmad T, Kaur M, Ahmad I, Sonia S. A review of various image enhancement and noise removal methods. International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Punjab, India. 2016 Mar; 6(3).
  • Chen J, Benesty J, Huang Y, Doclo S. New insights into the noise reduction Wiener filter. IEEE Transactions on Audio, Speech, and Language Processing. 2006; 14.
  • Chan T, Ma K-K, Li-Hui C. Tri-state median filter for image denoising. IEEE Transactions on Image Processing. 1999 Dec; 8:1834–8.
  • Peng S. L.Fuzzy filtering for mixed noise removal during image processing. Fuzzy Systems, IEEE World Congress on Computational Intelligence. 1994; 1:89–93.
  • Liu Y, Shen T, Xinyi W. Image Restoration Using Gau ssian Particle Filters. IEEE Transactions Conference on Computational Intelligence and Security, International Conference. 2007. p. 391–4.
  • Shreyamsha BK. Image denoising based on Gaussian/ bilateral filter and its method noise thresholding. Original Paper- Signal, Image and Video Processing.2013Nov; 7:1159–72.
  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing. 2004 Apr; 13:600–12.
  • Rafael C. Gonzalez and Richards E. Woods. Digital Image Processing, 2ndedition, Pearson Prentice Hall. 2005; 6:304– 64.
  • Wang Z, Alan C. Bovik. Handbook of Modern Image Quality Assessment. New York, Morgan & clay pool. 2006.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.