WuYang

http://white.stanford.edu/teach/index.php/Psych221_Project_Suggestions#Psf_analysis_and_image_deblurring_using_a_simulated_camera_lens Tony Wu, Samuel Yang

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Psf analysis and image deblurring using a simulated camera lens

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Introduction

Motivate the problem. Describe what has been done in the past.

The image capture process in often produce a sampled image that contains a blurred signal due to various optical parameters, including the lens and sensor objects. With the knowledge of the blur, which is fully characterized by a set of point spread functions, one for each position in the scene, it is possible to recover a sharp image by deblurring the captured imaging using the known point spread functions using deconvolution. In this way, it is possible to computationally correct for optical aberrations in the camera system.

Background

What is known from the literature.

[1] discusses the use of deconvolution to compensate for lens aberrations. Some methods are available which attempt to indirectly estimate the point spread function (PSF), such as the work in [2] which uses sharp edges to estimate the PSF, but this is challenging and requires regularization which can result in oversmoothing. More accurate approaches for determining the PSF involve directly measuring the PSF through some calibration process, but this can be time consuming. In [3], the authors develop a method for extending a PSF calibration measurement done at a single depth, to other depths, greatly minimizing the amount of calibration data necessary for acquiring the PSF data. To simplify this process further, the authors in [4] developed a method for calibration relying only on imaging a binary white noise target, and introduce the use of a novel cross channel prior in their optimization formalization for estimating the PSFs. FInally, in [5] the concept of using a calibration target for forming a well posed inverse problem for solving the the PSFs is extended to perform with sub pixel accuracy. Overall, these methods involve accurate estimation of the spatially varying PSFs and then deconvolution to remove the blur introduced by the optics.

Methods

Describe techniques you used to measure and analyze. Describe the instruments, and experimental procedures in enough detail so that someone could repeat your analysis. What software did you use? What was the idea of the algorithms and data analysis?

Image Simulations

All data used in this project was simulated using Image Systems Engineering Toolbox (ISET) [1].

We used the Zemax ray tracing data found in rtZemaxExample.mat.

Results

Organize your results in a good logical order (not necessarily historical order). Include relevant graphs and/or images. Make sure graph axes are labeled. Make sure you draw the reader's attention to the key element of the figure. The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.

Conclusions

Describe what you learned. What worked? What didn't? Why? What should someone next year try?

References

List references. Include links to papers that are online.

[2] Scalettar, B. A., et al. "Dispersion, aberration and deconvolution in multi‐wavelength fluorescence images." Journal of microscopy 182.1 (1996): 50-60.

[3] Joshi, Neel, Richard Szeliski, and David Kriegman. "PSF estimation using sharp edge prediction." Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008.

[4] Shih, Yichang, Brian Guenter, and Neel Joshi. "Image enhancement using calibrated lens simulations." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 42-56.

[5] Heide, Felix, et al. "High-quality computational imaging through simple lenses." ACM Transactions on Graphics (TOG) 32.5 (2013): 149.

[6] Delbracio, Mauricio, Pablo Musé, and Andrés Almansa. "Non-parametric sub-pixel local point spread function estimation." Image Processing On Line (2012).

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.

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

Appendix II

(for groups only) - Work breakdown. Explain how the project work was divided among group members.