1 / 16

Artistic Edge and Corner Enhancing Smoothing

This paper presents a novel image processing method that improves edge and corner details while reducing texture noise, generating images reminiscent of paintings. It generalizes the Kuwahara filter and introduces a Value-and-Criterion Filter Structure (VCFS) to better preserve visual integrity. The study compares the proposed method to existing techniques, highlighting its efficiency in maintaining sharpness and clarity. Experimental results validate the method's ability to enhance images significantly without compromising essential features, making it a valuable tool in image editing and analysis.

serge
Download Presentation

Artistic Edge and Corner Enhancing Smoothing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Artistic Edge and Corner Enhancing Smoothing Giuseppe Papari Nicolai Petkov Patrizio Campisi

  2. ABSTRACT • 1) absence of texture details 2) increased sharpness of edges as compared to photographic images • generalizes both the well known Kuwahara filter and the more general class of filters known as VCFS. • VCFS: value and criterion filter structure Value-and-criterion filters have a `value' function (V) and a `criterion' function (C), each operating separately on the original image, and a `selection' operator (S) acting on the output of C. The selection operator chooses a location from the output of C, and the output of V at that point is the output of the overall filter.

  3. OUTLINE • INTRODUCTION • KUWAHARA FILTER AND EXTENSIONS • PROPOSED OPERATOR • EXPERIMENTAL RESULTS

  4. INTRODUCTION • Linear low-pass filtering strongly attenuates high-frequency components, not only noise, but also edges and corners, are smoothed out. • There has been a remarkable effort to find a nonlinear operator able to remove texture and noise, while preserving edges and corners. • ECPS: edge and corner preserving smoother • Ex : median filtering, morphological analysis, bilateral filtering

  5. INTRODUCTION • current work: In a specific aspect of ECPSs, their ability to produce images that are visually similar to paintings. • algorithm makes use of: 1) a different set of weighting subregions for computing local averages 2) a different combination criterion which generalizes the minimum standard deviation rule and which does not suffer the above mentioned ill-posedness.

  6. KUWAHARA FILTER AND EXTENSIONS • A. Review of the Kuwahara Filter

  7. KUWAHARA FILTER AND EXTENSIONS • A. Review of the Kuwahara Filter MSDC: minimum standard deviation criterion

  8. KUWAHARA FILTER AND EXTENSIONS • B. Limitations of the Kuwahara Filter Fig.2

  9. PROPOSED OPERATOR

  10. PROPOSED OPERATOR MSDC: minimum standard deviation criterion

  11. PROPOSED OPERATOR

  12. EXPERIMENTAL RESULTS • A. Comparison With Existing Approaches • Fig.8 • Fig.9 • Fig.10 • Fig.11 • Fig.12 • Fig.13 • Fig.14

  13. EXPERIMENTAL RESULTS • B. Influence of the Parameters

  14. EXPERIMENTAL RESULTS • B. Influence of the Parameters

  15. EXPERIMENTAL RESULTS • B. Influence of the Parameters

  16. Thank you for your listening! 2007.12.18

More Related