Ademola-IdowuKhwajaGhosh
Analysis of Real Camera Lenses
Atinuke Ademola-Idowu, Ayesha Khwaja, Pallabi Ghosh
Introduction
Background
Methods
Pattern:
Figure1: pattern used by delbraciao
Results
Using the algorithm as described above the PSF of the camera system was calculated as shown in the figure below:
Next we also magnify each block so that the nature of the PSF is visible more clearly, and show the results in the next figure.
We see that there is a lot of noise in the estimated PSF. This noise can be attributed to the erroneous selection of points while trying to remove perspective distortion. The process was manual, and could have been prone to human error. Although we tried to remove this error through multiple selections of points, it improved the results slightly, but didn’t remove it totally. That is the reason why, selection of different regions while doing the division of Fourier transforms, gave different erroneous regions in the final PSF estimation.
Testing for the validity of estimated PSF
To check whether our PSF estimation is correct, we generated 5 patterns, as shown below
We captured the images of these 5 patterns using 3 different camera settings with 3 different f numbers. We took the Fourier transform of each pattern and multiplied it with the Fourier Transform of the PSF. Then we took the inverse Fourier transform and compared it to the captured images. The following figure shows the results for the radial pattern. The first column is the pattern, the second column shows captured images at F# 5.6,18 and 34 and the third column shows the corresponding convolved images.
Theoretically they should be equivalent, but due to a number of factors they are not. For example the exposure duration of the captured image of the test pattern is different from the exposure duration of the captured image from which the PSF is actually computed. The exposure duration of the test pattern is larger, hence it is brighter and more blurry.
But there are some factors which are really similar in the two sets. For example, the middle column consisting of captured image, has the 1st image as the least blurry. The blurriness increases as we go down the column. The same observation can be made in the last column as well. This shows the similarity in their nature, although due to factors mentioned above they look really different.
Conclusions
The project led us to 3 main concluding points
- The results in figure 3 shows that the central block has a symmetric PSF, whereas it is assymetric in the corner blocks and bent toward the corners. That is the characteristics of any PSF. So our results seem to be correct.
- Our algorithm uses the white image to do illumination control, hence it is robust to illumination changes
- But on the other hand our PSF estimation is noisy which causes the huge difference in captured and convolved images in figure 5. We would try to reduce the noise in the future. Including the camera characteristics in the calculation might reduce the noise. Also better method of selection of points to remove perspective distortion can be useful in noise reduction.