WuYang - Revision history

imported>Projects221: /* Image Simulations */

2014-03-19T06:49:00Z

Image Simulations

← Older revision		Revision as of 06:49, 19 March 2014
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	We used the Zemax ray tracing data found in rtZemaxExample.mat.		We used the Zemax ray tracing data found in rtZemaxExample.mat.
			In all of the simulations, we used 1um X 1um pixels with a 512 pixel X 512 pixel resolution sensor. Autoexposure was used to obtain the simulated photos.

	== Image Formation Model ==		== Image Formation Model ==

imported>Projects221: /* Measuring Point Spread Functions */

2014-03-19T06:47:13Z

Measuring Point Spread Functions

← Older revision		Revision as of 06:47, 19 March 2014
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	== Measuring Point Spread Functions ==		== Measuring Point Spread Functions ==
	To accurately measure the point spread functions, we simulated an image of a grid of points, shown in Figure 2a. We ran this simulation with no sensor noise, so as to get a measurement most resembling the PSFs as measured in the ideal case. Afterwards, we cropped the resulting image to get a PSF for each region of the output image. Figure 2b shows cropped images of the PSFs, which are spatially varying.		To accurately measure the point spread functions, we simulated an image of a grid of points spaced 32 pixels apart, shown in Figure 2a. We ran this simulation with no sensor noise, so as to get a measurement most resembling the PSFs as measured in the ideal case. Afterwards, we cropped the resulting image to get a PSF for each region of the output image. Figure 2b shows cropped images of the PSFs, which are spatially varying.

	== Spatially Varying Deconvolution ==		== Spatially Varying Deconvolution ==

imported>Projects221: /* Appendix II */

2014-03-17T16:34:59Z

Appendix II

← Older revision		Revision as of 16:34, 17 March 2014
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	=Appendix II=		=Appendix II=
	~~(for groups only) - Work breakdown. Explain how the project work was divided among group members.~~		Division of labor:

	Tony Wu - wrote PSF acquisition code, implemented read and dark noise sweeps, wrote wiki documentation, made figures and slides		Tony Wu - wrote PSF acquisition code, implemented read and dark noise sweeps, wrote wiki documentation, made figures and slides

	Samuel Yang - wrote PSF acquisition code, implemented weighting function, wrote wiki documentation, made figures and slides		Samuel Yang - wrote PSF acquisition code, implemented weighting function, wrote wiki documentation, made figures and slides

imported>Projects221: /* Appendix II */

2014-03-16T23:09:09Z

Appendix II

← Older revision		Revision as of 23:09, 16 March 2014
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	(for groups only) - Work breakdown. Explain how the project work was divided among group members.		(for groups only) - Work breakdown. Explain how the project work was divided among group members.

	Tony Wu		Tony Wu - wrote PSF acquisition code, implemented read and dark noise sweeps, wrote wiki documentation, made figures and slides

	Samuel Yang		Samuel Yang - wrote PSF acquisition code, implemented weighting function, wrote wiki documentation, made figures and slides

imported>Projects221: /* Conclusions */

2014-03-16T23:02:07Z

Conclusions

← Older revision		Revision as of 23:02, 16 March 2014
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	=Conclusions=		=Conclusions=
	A method to unblur images by deconvolution with the spatially varying PSF was implemented. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image, as evident in a lower VSNR score. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. Our conclusions were found to hold with varying levels of both read noise and dark noise. In all cases, the images were sharper but noisier. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.		A method to unblur images by deconvolution with the spatially varying PSF was implemented and tested on simulated camera data. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image, as evident in a lower VSNR score. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. Our conclusions were found to hold with varying levels of both read noise and dark noise. In all cases, the images were sharper but noisier. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.

	=References=		=References=

imported>Projects221: /* Conclusions */

2014-03-16T23:01:48Z

Conclusions

← Older revision		Revision as of 23:01, 16 March 2014
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	=Conclusions=		=Conclusions=
	A method to unblur images by deconvolution with the PSF was implemented. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.		A method to unblur images by deconvolution with the spatially varying PSF was implemented. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image, as evident in a lower VSNR score. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. Our conclusions were found to hold with varying levels of both read noise and dark noise. In all cases, the images were sharper but noisier. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.

	=References=		=References=

imported>Projects221: /* Appendix I */

2014-03-16T22:49:52Z

Appendix I

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	=Appendix I=		=Appendix I=
	Upload source code, test images, etc, and give a description of each link. In some cases, your acquired data may be too large to store practically. In this case, use your judgement (or consult one of us) and only link the most relevant data. Be sure to describe the purpose of your code and to edit the code for clarity. The purpose of placing the code online is to allow others to verify your methods and to learn from your ideas.


	Source code: http://white.stanford.edu/teach/images/a/ad/Project.zip		Source code: http://white.stanford.edu/teach/images/a/ad/Project.zip

imported>Projects221: /* Appendix I */

2014-03-16T22:49:36Z

Appendix I

← Older revision		Revision as of 22:49, 16 March 2014
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			Source code: http://white.stanford.edu/teach/images/a/ad/Project.zip

	Image Systems Engineering Toolbox http://imageval.com/		Image Systems Engineering Toolbox http://imageval.com/

imported>Projects221: /* References */

2014-03-16T22:33:50Z

References

← Older revision		Revision as of 22:33, 16 March 2014
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	=References=		=References=
	~~List references. Include links to papers that are online.~~

	[http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2818.1996.122402.x/full#content] Scalettar, B. A., et al. "Dispersion, aberration and deconvolution in multi‐wavelength fluorescence images." Journal of microscopy 182.1 (1996): 50-60.		[http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2818.1996.122402.x/full#content] Scalettar, B. A., et al. "Dispersion, aberration and deconvolution in multi‐wavelength fluorescence images." Journal of microscopy 182.1 (1996): 50-60.

imported>Projects221: /* Conclusions */

2014-03-16T22:33:26Z

Conclusions

← Older revision		Revision as of 22:33, 16 March 2014
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	=Conclusions=		=Conclusions=
	~~Describe what you learned. What worked? What didn't? Why? What should someone next year try?~~

	A method to unblur images by deconvolution with the PSF was implemented. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.		A method to unblur images by deconvolution with the PSF was implemented. We qualitatively see a sharpening of the image as well as a quantitative increase in the MTF50, a measurement of the resolution, with this method. However, the deconvolution also amplified the noise in the image. While using a triangle window function to stitch the deconvolved images together worked better than the square window function, this was not able to reduce the amount of noise amplified by the deconvolution. In the future, a more advanced method of deconvolution such as [4] to keep the noise amplification to a minimum while maximizing the MTF50.