Multi resolutionRefinement
- This protocol requires images of size which size power of 2. If this is not the case, rescale the images.
- Choose the image size to be used in the multi-resolution approach, and prepare the data for the angular assignment and reconstruction steps:
- Perform a discrete angular assignment:
- Perform a continuous angular assignment to refine the discrete assignments:
- Mask the images for 3D reconstruction.
- Perform a 3D reconstruction.
- Correct the images for the effects of the CTF amplitude. (This step is optional.)
- Calculate the resolution of the current model.
- Post-process the reconstructed volume by masking, low-pass filtering, and positioning its centre-of-mass at the origin. Subsequent masking with a user provided mask is optional.
- Repeat previous steps. Iterate until the resolution does not improve anymore. If the next iteration uses a larger image size than the current one, re-scale the reference volume correspondingly:
- Input
- gallery of images
- user defined mask
- reference volume
File multi_res.tar.gz contains all the images, the reference volume, the mask and the xmipp_protocol_multires.py script with suitable parameters. This example consists of about 5,000 simulated ribosome images (128x128 pixels) classified by ML3DClassification protocol. These images belong to the class with the elongation factor G (EFG).
- Output
- 3D reconstruction
- Document file with assigned angles
File output_multi_res.tar.gz contains the results of executing theMulti Resolution script. In order to reduce the size of the tar.gz file only has been included the three first and the last iteration (30). Only the three first iterations have all the output files, so are the only one in which the discret angular assigment summary could be visualized
Pressanalyze results button for visualizing the results of angular refinement.
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| 3D intial reference (bottom-left) and 3D reconstructed map of the last iteration (bottom-right) |
- output_highres3d.jpg: