visual sense of sharpness. The averages” start out blurry, start aligning between = 5 g “advanced to 8, and then look the same from that point on. For the root data set, the results are While the frequency threshold algorithm performed well, the variance algorithm finds the averages to be the In misaligned “sharpest”! this case the variance contributed by the noise was greater than the variance contributed by the sharp features of interest. The variance method is not expected to perform consistently from one data set to another because it treats sharp features and high frequency noise on equal footing. Note that the same frequency parameters were the same for both data sets (Rin= and w/32 Rout = These values were found experimentally through and error. We conclude that these w/16). parameters should work for TXM images, although certainly more tests are needed. “typical” Now that we have gained some insight into quality evaluation, there are several directions for continued research. The literature is rich with various sharpness algorithms, including several which claim to be immune to noise [4], [5]. There also exist “no-reference” sharpness metrics which can align images without any prior knowledge of the images (i.e. it is not necessary to manually adjust parameters) [4], [5]. By implementing more sophisticated and robust algorithms, we can improve the ease and reliability of our GUI’s. In addition to adding more sophisticated algorithms, we would like to completely automate the advanced average process. In the ideal scenario, when images are loaded into the Advanced Average GUI, the computer would automatically find the ideal value, and then g return only the sharp average. One way to achieve this would be to 1) calculate the “advanced average” at a default (say 8), 2) evaluate the sharpness, 3) calculate the g g = “advanced average” at adjacent (g 8 and 9), 4) evaluate these sharpnesses, and 5) continue g’s = g =
visual sense of sharpness. The “advanced averages” start out blurry, start aligning between g = 5 to 8, and then look the same from that point on. For the root data set, the results are While the frequency threshold algorithm performed well, the variance algorithm finds the misaligned averages to be the “sharpest”! In this case the variance contributed by the noise was greater than the variance contributed by the sharp features of interest. The variance method is not expected to perform consistently from one data set to another because it treats sharp features and high frequency noise on equal footing. Note that the same frequency parameters were the same for both data sets (Rin= w/32 and Rout = w/16). These values were found experimentally through and error. We conclude that these parameters should work for “typical” TXM images, although certainly more tests are needed. Now that we have gained some insight into quality evaluation, there are several directions for continued research. The literature is rich with various sharpness algorithms, including several which claim to be immune to noise [4], [5]. There also exist “no-reference” sharpness metrics which can align images without any prior knowledge of the images (i.e. it is not necessary to manually adjust parameters) [4], [5]. By implementing more sophisticated and robust algorithms, we can improve the ease and reliability of our GUI’s. In addition to adding more sophisticated algorithms, we would like to completely automate the advanced average process. In the ideal scenario, when images are loaded into the Advanced Average GUI, the computer would automatically find the ideal g value, and then return only the sharp average. One way to achieve this would be to 1) calculate the “advanced average” at a default g (say g = 8), 2) evaluate the sharpness, 3) calculate the “advanced average” at adjacent g’s (g = 8 and g = 9), 4) evaluate these sharpnesses, and 5) continue
Bolgert_Research_Paper.docx