Filtering and Reconstruction System for Gray Forensic Images
Authors: Ahd Aljarf, Saad Amin
Abstract:
Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images.
Keywords: Image Filtering, Image Reconstruction, Image Processing, Forensic Images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337677
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2217References:
[1] S. Umbaugh, “Computer Vision and Image Processing: a practical approach using CVIP tools”, UK: Prentice Hall PTR, 1998
[2] M. Breeuwsma, “Forensic imaging of embedded systems using JTAG (boundary-scan),” Digital Investigation, vol. 3, pp. 32-42, 2006
[3] D. Venkateshwar, “Implementation and Evaluation of Image Processing Algorithm on FPGA,” International Journal of Theoretical and Applied Computer Sciences Processing, vol. 1, pp. 1-10, 2006
[4] C. Behrenbruch, “Image filtering techniques for medical image postprocessing: an overview,” The British Journal of Radiology, vol. 77, pp. 2-6, 2004
[5] E. Brooks, and E. Comber, “Digital imaging and image analysis applied to numerical applications in forensic hair examination,” Journal of Science and Justice, vol. 51, pp. 28–37, 2010
[6] Z. Geradts, “Content Based Information Retrieval in Forensic Image Databases,” Unpublished PhD thesis. Netherland: University of Utrecht, 2001
[7] C. Akujuobi, “The effects of different wavelets on image reconstruction,”Proceedings of the IEEE. Bringing Together Education, Science and Technology, vol. 55, pp. 5-10, 1996
[8] D. Janecki, “Gaussian filters with profile extrapolation,” Precision Engineering, vol. 35, pp. 602– 606, 2011
[9] J. Rane, J. Remus and G. Sapiro, "Wavelet-domain reconstruction of lost blocks in wireless image transmission and packet-switched networks," in Proc. ICIP , vol. 1, pp.309-312, 2002
[10] R. Silva, F. Prado and I. Caputo, “The forensic importance of frontal sinus radiographs,” Journal of Forensic and Legal Medicine, vol.16, pp.18–23, 2008
[11] G. Yang, P. Burgerb, D. Firmin and S. Underwoodad “Structure adaptive anisotropic image filtering,” Image and Vision Computing, vol. 14, pp. 135- 145, 1996
[12] H. Aziz, “Medical Image Reconstruction Using Wavelet and Multi – Wavelet Algorithms,” Unpublished dissertation. Coventry: Coventry University, 2010
[13] P. Bauszat, M. Eisemann and M. Magnor, “Guided Image Filtering for Interactive High-quality Global Illumination,”Computer Graphics forum, vol. 30, pp.1361-1368, 2011