Edge ringing artifact suppression methods and apparatuses
In some embodiments, the present invention relates to methods or suppressing edge ringing in images. For example, in some embodiments a method of processing an image to suppress ringing and broadened edges induced by image correction processing, includes high-pass filtering a first image to obtain a second image, processing said second image including applying non-linear apodization to said second image to obtain a third image, low-pass filtering said first image to obtain a fourth image, and combining the third image and the fourth image to obtain an output image, wherein the output image is characterized by having reduced edge-response sidelobes as compared to the first images. In some embodiments, the present invention relates to devices comprising means and/or modules to suppress edge ringing in images.
1. A method of processing an image to suppress edge ringing induced by image correction processing, comprising:
high-pass filtering a first image to obtain a second image having a set of data samples;
processing said second image including applying non-linear apodization to said second image to obtain a third image, wherein applying non-linear apodization comprises
determining a weight for each data sample in said set of data samples,
calculating a new value for each data sample in said set of data samples, the new value based on said determined weight of each data sample and on one or more neighboring data samples, and wherein the third image is obtained from the new values of each data sample in said set of data samples;
low-pass filtering said first image to obtain a fourth image; and
combining the third image and the fourth image to obtain an output image.
2. The method of claim 1 , wherein the output image is characterized by having reduced edge-response sidelobes as compared to the first image.
3. The method of claim 1 , wherein applying non-linear apodization comprises processing said second image using a processing technique selected from the group of Spatially Variant Apodization (SVA), Adaptive Sidelobe Reduction (ASR), adaptive Kaiser windowing, and dual apodization.
4. The method of claim 1 , wherein applying non-linear apodization comprises Spatially Variant Apodization (SVA).
5. The method of claim 1 ,
wherein determining a weight for each data sample comprises determining a weight for each data sample as a function of the negative of a selected data sample divided by the sum of said one or more neighboring data samples of the selected data sample, wherein the weight for each data sample is limited to a predetermined range, and
wherein calculating a new value for each data sample comprises adding to each data sample the product of the determined weight and the sum of said one or more neighboring data samples.
6. The method of claim 1 , further comprising correction processing an input image to compensate for sensor induced image degradation to produce said first image, wherein said correction processing comprises using a Modulation Transfer Function Correction (MTFC) technique.
7. The method of claim 6 , wherein said Modulation Transfer Function Correction (MTFC) technique comprises Fourier transforming said input image, applying a function to the transformed image to form a resulting image, said applied function representative of an inverse Modulation Transfer Function of an imaging system used to collect said input image, and inverse Fourier transforming the resulting image to form said first image.
8. The method of claim 1 , wherein said high-pass filtering comprises using a Hanning high-pass filter.
9. The method of claim 1 , wherein said low-pass filtering comprises using a Hanning low-pass filter.
10. The method of claim 1 , wherein said first image comprises an electro-optical image.
11. The method of claim 1 , wherein said first image comprises a biomedical image.
12. A method of applying non-linear apodization processing to a biased image to suppress edge ringing characteristics in an output image, the method comprising:
unbiasing at least a portion of the biased image to obtain an unbiased image having a set of data samples;
applying non-linear apodization to the unbiased image to obtain a second image, wherein applying non-linear apodization comprises determining a weight for each data sample in said set of data samples, and calculating a new value for each data sample in said set of data samples, the new value based on said determined weight of each data sample and on one or more neighboring data samples; and
reversing the unbiasing to the second image to form the output image.
13. The method of claim 12 , wherein the non-linear apodization method is Spatially Variant Apodization (SVA).
14. The method of claim 12 , wherein the unbiasing comprises high-pass filtering.
15. The method of claim 12 , wherein reversing the unbiasing comprises low-pass filtering.
16. The method of claim 12 , wherein the unbiasing comprises unbiasing a pixel based on properties of a local area.
17. The method of claim 16 , wherein the local area is greater than or equal to two pixels by two pixels.
18. An image processing system comprising:
at least one processor configured to execute instructions for processing imagery;
memory for storing instructions for processing imagery to suppress edge ringing characteristics, the instructions comprising
a first processing module configured to un-bias at least a portion of a biased image to obtain an unbiased image having a set of data samples;
a second processing module configured to apply non-linear apodization to the unbiased image to obtain a second image, the non-linear apodization including determining a weight for each data sample in said set of data samples, and calculating a new value for each data sample, the new value based on said determined weight of each data sample and on one or more neighboring data samples to each data sample; and
a third processing module configured to reverse the unbiasing to the second image to obtain an output image.
19. The system of claim 18 , further comprising a pre-processing module configured to apply a resolution-enhancing correction to an input image to obtain said first image.
20. The system of claim 19 , wherein the resolution-enhancing correction comprises a Modulation Transfer Function correction.
21. The system of claim 18 , further comprising a sensor module configured to obtain said input image.
22. The system of claim 21 , further comprising a sensor configured to provide said input image to said sensor module.
23. The system of claim 21 , wherein said sensor module is configured to obtain biomedical images.
24. The system of claim 22 , wherein said sensor comprises an imaging device.
25. The system of claim 22 , wherein said sensor comprises one or more lenses and at least one aperture.
26. The system of claim 22 , wherein said sensor comprises a camera.
27. The system of claim 22 , wherein said sensor comprises an x-ray imaging device.
28. The system of claim 22 , wherein said sensor comprises an ultrasound imaging device.
29. The system of claim 22 , wherein said sensor comprises a magnetic resonance imaging device.
30. The system of claim 18 , wherein the non-linear apodization processing comprises Spatially Variant Apodization.
31. The system of claim 18 , further comprising a display device to display the output image.
32. A computer having a memory, the memory having both an image processing program and a first image stored therein, the image processing program comprising:
instructions to un-bias at least a portion of a biased image to obtain an unbiased image having a set of data samples;
instructions to apply non-linear apodization to the unbiased image to obtain a second image wherein applying non-linear apodization includes determining a weight for each data sample in said set of data samples, and calculating a new value for each data sample in said set of data samples, the new value based on said determined weight of each data sample and on one or more neighboring data samples; and
instructions to reverse the unbiasing to the second image to obtain an output image.
33. The computer of claim 32 , further comprising a instructions to apply a modulation transfer function correction to the first image.
34. The computer of claim 32 , wherein the non-linear apodization method is Spatially Variant Apodization.
35. A non-transitory machine readable medium comprising instructions for processing multimedia data that upon execution causes a machine to:
un-bias a biased image to obtain an unbiased image;
apply non-linear apodization to said unbiased image to obtain a second image, wherein applying non-linear apodization includes
determining a weight for each data sample in a set of data samples in the unbiased image, and
calculating a new value for each data sample in said set of data samples, the new value based on said determined weight of each data sample and on one or more neighboring data samples; and
reverse the unbiasing to the second image to obtain an output image.