FEM: feature-enhanced map

Abstract

A method is presented that modifies a 2 m F obs − D F model σ A -weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2 m F obs − D F model σ A -weighted map.

Keywords

Affiliated Institutions

• University of California, Berkeley US
• Centre National de la Recherche Scientifique FR
• Jožef Stefan International Postgraduate School SI
• Université de Lorraine FR
• Los Alamos National Laboratory US
• Jožef Stefan Institute SI
• Lawrence Berkeley National Laboratory US
• Oak Ridge National Laboratory US
• Inserm FR
• Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins SI
• Institut de génétique et de biologie moléculaire et cellulaire FR

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