Noise Reduction in Digital Images Lana Jobes Research Advisor: Dr. Jeff Pelz.
21
Noise Reduction in Digital Images Lana Jobes Research Advisor: Dr. Jeff Pelz
-
date post
20-Dec-2015 -
Category
Documents
-
view
223 -
download
5
Transcript of Noise Reduction in Digital Images Lana Jobes Research Advisor: Dr. Jeff Pelz.
Noise Reduction in Digital Images
Lana Jobes
Research Advisor: Dr. Jeff Pelz
Chart 2 of 21Lana Jobes
Introduction
• Long Exposure Times are a Problem with CCD Arrays
- Objectionable Noise Present in Images• Caused by Thermal Excitation in Camera Electronics
- Noise is Additive and Band Dependent
• Research Has Developed Technique to Reduce This Noise
Chart 3 of 21Lana Jobes
Introduction
• Kodak Recommends Using Exposure Times Less than 1/4 Second
- Applies to High-End and Low-end Cameras
• Image Noise Increases Dramatically with Long Exposure Times
• Limits Usage in Low-Light Situations and Limits Effective Sensitivity
Chart 4 of 21Lana Jobes
Methods
• Characterization of the Noise
- 48 ‘Dark’ Images Obtained Using Kodak DCS 315 Camera
- 12 Different Exposure Times Ranging 1/6 to 30 Seconds
- 2 Images per Exposure Time on 2 Different Days
- Lens Cap On to Isolate Noise
30 Second Exposure Time
30 Second Exposure 15 Second Exposure
Chart 7 of 21Lana Jobes
Methods
• Identification of ‘Hot’ pixels
- Histograms of Each Color Channel
- Thresholds Chosen for Each Band• Red -- 40• Green -- 30• Blue -- 60
- Number of Hot Pixels > Threshold• Red -- 113,328 (7%)• Green -- 62,970 (4%)• Blue -- 135,949 (9%)
Chart 8 of 21Lana Jobes
Methods
• First Attempt to Reduce the Noise
- For Each ‘Hot’ Pixel, Located a ‘Cold’ One
0 5 10 15 5055 10 20 20 4020 5 65 10 3515 15 25 15 4510 40 35 20 50
0 1 2 3 4
0
4
3
2
1
- Created File Containing x,y Coordinates for Hot (2,2) and Cold Pixels (0,0)
- Process Images Substituting ‘Cold’ Value for ‘Hot’ One
Edge Artifact Discovered
Original Image Processed Image
Chart 10 of 21Lana Jobes
Methods
• Second Attempt to Reduce the Noise- Selective Median Filtering for ‘Hot’ Pixels- Sort Surrounding Pixels Within a Specified Radius- Replace ‘Hot’ Pixel with Median Value
0 13 10 10 180 10 10 13 120 12 65 11 100 10 10 15 100 11 10 20 10
0,0,0,0,0,10 ,10 ,10 ,10 ,10 ,10 ,10 ,10 ,10 ,10,11,11,12,12,13,13,15,18,20,65
Median Value
Chart 11 of 21Lana Jobes
Methods
• Determine Best Width or Radius for Filtering
- Noise in Green Channel Mostly Single Pixels
- Noise in Blue and Red in Clusters
- Decided to Use Channel Dependent Widths • 3x3 for Green • 7x7 for Red• 11x11 for Blue
Image Before Processing Image After Processing with Selective Median Filter
Original Image First Method Median Filtered
Chart 14 of 21Lana Jobes
Methods
• Transformation to CIE L*a*b* Color Space
- CIE L*a*b* is a Uniform Color Space
• Models Human Visual System
- ‘L*’ - Luminance Information
- ‘a*’ - Red/Green Information
- ‘b*’ - Yellow/Blue Information
Chart 15 of 21Lana Jobes
Methods
• Amount of Filtering Channel Dependent
- Light Filtering in L* Channel• Main Contributer to Sharpness Perception• Use Filter Radius of 1 Pixel• Filter 5% of Pixels
- Moderate Filtering in a* Channel• Small ‘Clumps’ of Noise• Use Filter Radius of 4 Pixels• Filter 15% of Pixels
Chart 16 of 21Lana Jobes
Methods
• Amount of Filtering Channel Dependent (con’t)
- Aggressive Filtering in b* Channel• Large ‘Clumps’ of Noise• Use Filter Radius of 6 Pixels• Filter Entire Channel
Image After Processingin RGB Space
Image After Processing in CIE L*a*b* Space
Original Image Image After Processing in CIE L*a*b* Space
Image After Processingin RGB Space
Image After Processing in CIE L*a*b* Space
Image After Processingin RGB Space
Image After Processing in CIE L*a*b* Space
Chart 21 of 21Lana Jobes
Conclusion
• Technique Developed That Significantly Reduces Additive Noise
- Use of Color Space Transformation
- Channel Dependent Noise Reduction
• Further Refinements Still Underway
• Patent Pending
