CS-467 Image processing and Computer Vision

Course Project 9

Goals: 1) to learn specific aspects of color image processing and BM3D filtering

Pepper.tif – the original image Pepper_GauColor01.tif – the image whose color channels are corrupted by white additive Gaussian noise with the standard deviation equal to 0.3σ where σ is the standard deviation of the corresponding original color channel, so there is a “color” noise there; Pepper_Gau01.tif – the image whose luminosity channel is corrupted by white additive Gaussian noise with the standard deviation equal to 0.3σ where σ is the standard deviation of the original luminosity channel, so there is a “monochromatic” noise there; Pepper_ImpRandColor-10.tif – the image whose color channels are separately corrupted by random impulse noise with the corruption rate 10%, so there is a “color” noise there; Pepper_ImpRand-10.tif – the image whose luminosity channel is corrupted by random impulse noise with the corruption rate 10%, so there is a “monochromatic” noise there. 1. Filter these 4 noisy images applying the rank-order EV filter with sub-optimal EV parameter to suppress Gaussian noise and differential rank impulse detector (DRID) followed by median filtering to filter impulse noise.

While to process images corrupted by monochromatic noise it is sufficient to filter a corresponding luminosity channel only, it is typically not enough to get rid of color noise.

You shall process images corrupted by color noise using the following two methods: 1) processing only a luminosity channel and 2) processing each color channel separately. Compare your results (your criterion is PSNR/RMSE) measured for luminosity channel and color channels.

2. You may use VEGA working on this project 3. (Bonus 50% extra credit) You may use your own earlier designed Matlab programs working on this project (just take care of color channel extraction after an image was read and merging them into a resulting image after the image is processed) 4. (Bonus 50% extra credit) Process images Pepper_GauColor01.tif and Pepper_Gau01.tif using the BM3D filter, just apply it not to an image “as it is”, but to its corresponding channels separately. Compare these results to the ones obtained using the rank-order EV filter. 5. Prepare a brief technical report based on the measured PSNRs. Put your resulting images and the report in the subfolder Project 9 (you need to create it) located in the designated folder (named by your last name) in the folder \\sfs01\classes\CS 467 001\Class Data (The folder \\sfs01\classes is mapped from all the lab computers, so you can easily find it through File Explorer (Computer) in Windows 7. A shortcut to the Classes folder is also available on the desktop of the lab computers.