Sorry for being late to this party, but life kept me very busy recently…
This is my “best” result with enfuse, obtained in the following way:
- I saved images at 1EV intervals, from -3EV to +3EV, in perceptually-encoded sRGB and 16 bit TIFFs
- merged the images with the following command:
--exposure-weight=1 --saturation-weight=0 --blend-colorspace=IDENTITY --gray-projector=luminance --depth=r32 --output=CRW_3486-3488-enfuse.tif CRW_3486-3488??.tif
Here is the result:
The result is quite flat, particularly in the sky…
Recently I have started to play with another method, which is based on the bilateral filter. This method is not new, and there is quite a lot of scientific literature on the subject (see for example this and this articles). It boils down to performing a two-level decomposition of the image, where a blurred version of the image is subtracted to the original one to isolate the high-frequency details. The contrast of the blurred image is then flattened to compress the overall dynamic range, and finally the high-frequency level is added back to restore the local details.
The key is in the blur operator: when using a simple gaussian kernel, the result will show visible halos around strong edges. This problem can be mitigated, or even practically removed, by using an edge-aware blur operator like the bilateral filter. Such blur operators are able to “distinguish” between fine-grained texture (that gets blurred) from hard edges (that are left unchanged). As a result, the high-frequency level will contain the fine-grained details, but not the hard edges, which are kept in the blurred version.
Here is what I could obtain with a simple application of the above method:
The result gets even better after restoring a bit of contrast to the merged image:
Here is a PhotoFlow preset that automates the procedure:
SH-recovery-bilateral.pfp.zip (1.8 KB)