  Extreme UltraViolet images of the corona contain information over a large
range of spatial scales, and different structures such as active regions, quiet
Sun and filament channels contain information at very different brightness
regimes. Processing of these images is important to reveal information, often
hidden within the data, without introducing artifacts or bias. It is also
important that any process be computationally efficient, particularly given the
fine spatial and temporal resolution of Atmospheric Imaging Assembly on the
Solar Dynamics Observatory (AIA/SDO) , and consideration of future
higher-resolution observations. A very efficient process is described here
which is based on localized normalizing of the data at many different spatial
scales. The method reveals information at the finest scales, whilst maintaining
enough of the larger-scale information to provide context. It also
intrinsically flattens noisy regions and can reveal structure in off-limb
regions out to the edge of the field of view. The method is also successfully
applied to a white light coronagraph observation.
