By Ryszard S. Choraś
This publication collects a chain of analysis papers within the sector of photograph Processing and Communications which not just introduce a precis of present know-how but in addition supply an outlook of strength function difficulties during this region. the major aim of the booklet is to supply a set of finished references on a few fresh theoretical improvement in addition to novel functions in photograph processing and communications. The publication is split into components and provides the complaints of the 8th foreign photograph Processing and Communications convention (IP&C 2016) held in Bydgoszcz, Poland September 7-9 2016. half I offers with photograph processing. A accomplished survey of alternative equipment of snapshot processing, desktop imaginative and prescient can be provided. half II bargains with the telecommunications networks and desktop networks. functions in those parts are considered.
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Extra resources for Image Processing and Communications Challenges 8: 8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings
8671, pp. 212–219. Springer, Heidelberg (2014). 1007/978-3-319-11331-9 26 4. : Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition (ICPR), pp. 2366–2369. IEEE (2010) 5. IJG: Independent JPEG Group (2015). org/ 6. : A distortion measure for blocking artifacts in images based on human visual sensitivity. IEEE Trans. Image Process. 4(6), 713–724 (1995) 7. : Eﬀects of JPEG compression on image classiﬁcation. Int. J. Remote Sens. 24(7), 1535–1544 (2003) 8. : Study and comparison of various image edge detection techniques.
1 50 M. Nieniewski and P. Zajaczkowski Fig. 3. Despeckling results of NLMC, NLMST, NLMDT for carotid image of Fig. 4 1 iter. 2 0 51 1 iter. 4 4 iters. 2 0 4 iters. 20 40 β Fig. 4. The universal quality index Q and structural similarity index SSIN for the breast image of Fig. 1 calculated as a function of the strength parameter β for 1 and 4 iterations of NLMST and NLMDT Table 3. 659 0 0 GAE (1–4) 0 52 4 M. Nieniewski and P. Zajaczkowski Conclusions Replacing the structure tensor by the diﬀusion tensor in calculating the weights used in the NLM leads to the improvement of the despeckled images.
728 0 0 GAE (1–4) 0 All the calculations are coded in Matlab with the code for NLMC originating from . Despeckling the breast image from Fig. 1 is illustrated in Fig. 2 for the three NLM ﬁlters under consideration and for 1 – 4 iterations. Similarly, despeckling the carotid artery image from Fig. 1, is depicted in Fig. 3. Analyzing the above images one comes to the conclusion that the NLMLST and NLMC give very similar results. The ﬁltered images, particularly for 3 and 4 iterations are to a large extent despeckled, but at the same time exhibit ﬂat areas with very distinct contours.
Image Processing and Communications Challenges 8: 8th International Conference, IP&C 2016 Bydgoszcz, Poland, September 2016 Proceedings by Ryszard S. Choraś