Image Denoising using Spatial Domain Filters: A Quantitative Study Anmol Sharma Dr. Jagroop Singh Undergraduate Engineering Student Associate Professor, ECE Department DAV Institute of Engineering & Technology DAV Institute of Engineering & Technology
30
Embed
Image Denoising using Spatial Domain Filters: A Quantitative Study
o Presented the paper at IEEE International Congress on Image and Signal Processing & BioMedical Engineering and Informatics 2013 (CISP-BMEI 2013) held at Hangzhou, China, 16-18th December 2013 being the first author. Paper will soon appear on IEEE XPLORE online library. More details available on request.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Image Denoising using Spatial Domain Filters: A Quantitative StudyAnmol Sharma Dr. Jagroop Singh Undergraduate Engineering Student Associate Professor, ECE DepartmentDAV Institute of Engineering & Technology DAV Institute of Engineering & TechnologyJalandhar, Punjab, India. Jalandhar, Punjab, India.
Problem•Number of Filters available to remove noise. •Performance and correct use of any particular filter for any situation is still a matter of ongoing research.•Knowledge of toolset at hand is essential.
Image Denoising•An operation to estimate clean image from a degraded noise affected image.•Noise may be caused due to pixel corruption during acquisition, transmission or compression process. Also due to faulty hardware, poor lighting and motion blur. •Degradation and Restoration problem can be denoted mathematically as –
Noise Filters•Essentially inverse degradation models. •When applied to a corrupted image, can estimate the original image. •Divided into two types – Spatial Domain and Transform Domain.•Spatial Domain Filters fairly developed at the moment.•Mathematically,
Methodology•Noise was added to a grayscale image in a controlled fashion. •Corrupted image was obtained.•The corrupted image was subjected to all the available filters. •The best performing filter was decided according to the similarity measures used. •Process was repeated for all covered noise models.
Results•The simulations were performed in MATLAB. •Data was recorded in the form of tables and represented using graphs. •The filters scoring the highest value of PSNR as well as 2D Cross Correlation value was declared to be the best filter for that noise model.
Filter analysis using 2D Cross Correlation for Poisson Noise
Conclusion•Noise parameters were changed and various combinations tested to confirm results.•Number of filter parameters were tested, but the parameter with best results was used. •The procedure and tests were applied to other benchmark images like “Cameraman” and “Pout” to validate results.
Future Work•More generalised results are to be evaluated for each noise model, not just for any specific noise density levels. •Filter performance will be evaluated on images corrupted with more than one type of noise model. •A new unsupervised adaptive filter is in works based on median filter which would identify the noise model & density and calibrate it’s parameters accordingly.