[Playraw] Presolana (was: Salvaging a raw)

Morning, all,

I have not yet managed to produce anything better than the postings above, but here is an idea that looks promising so far:

In darktable, stack three or four haze removal modules on top of each other. That, in combination with clever masks, ought to work wonders!

Have fun!
Claes in Lund, Sweden

Although you can use the haze removal module of the darktable, I usually find out that I prefer to remove haze manually using tone curves with drawn masks. The following photo has been developed by such a technique.

DSC00989.ARW.xmp (7.3 KB)


Hi. I’m trying to use your xmp but Darktable tells me about wrong version of bilat filter (?), and the image results very dark. I’m running Darktable 2.2.5 on Arch Linux. Besides, I have no haze removal moduel, maybe it’s found in the latest git version?


@Dario It is probably in the git version. It should be in the correction group if you would like to know where to look for it.

By the way, thanks to everybody, now I have to learn something from your examples:)
I tried the RGB curves with the GIMP and I was able to get faster to a useful point, so it looks like it’s a feature Darktable is currently missing. I read a bug report about it.
I don’t know about the license of the raw, let’s say that you can use it but report me as original author, in the remote possibility you would use the image somewhere else:)


@Dario Here is a guide to licensing: PlayRaw stuff to keep in mind. You can apply it to anything you create, not limited to PlayRaws. Just a common way to communicate how to respect each another’s work.

Done in RT without tone mapping:

Salvage.pp3 (11.6 KB)

But boy this file sure behaved strangely…hypersensitive to the smallest changes!!


@Dario Which GIMP are you using? If you haven’t already installed 2.9.x, I suggest you try it. It has features that are essential for the type of edits you would need for this raw.

@Dario I’m just curious…what version of the image most closely matches what your eyes saw the day you took the photo? Your original? Your edit? Someone else’s edit?


Yes! I was wondering that too but forgot to ask.

Couldn’t agree more.

Well, actually the mountain range was quite blueish, I have to say that the raw from the camera reflects more or less faithfully what I saw that morning… Probably James’ version is the most faithful representation of what my brain did perceive. I estimate the mountains were about 20km from where I stood.
I’m using an Appimage of Gimp 2.9.5 from last year downloaded from this site - newer ones don’t run on my computer.




I used Darktable to match as closely as I could to the image you liked from Luminance HDR:

DSC00989.ARW.xmp (10.6 KB)

The also with a few tweaks to suit my own tastes:

DSC00989_01.ARW.xmp (18.4 KB)

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I am glad that I wasn’t too far off. I tend to exercise restraint when processing raws, so you could be more aggressive with the steps and arrive at your desired outcome. And examine @james’s pp3, although it seems like you prefer dt.

That is an okay version. If you have any trouble with the appimage, I am sure that @Carmelo_DrRaw would be happy to assist you.

Yes, I use the latest git version. Sorry for not mentioning it in my previous post.

My (probably late) take, RT.

DSC00989.ARW.pp3 (11.6 KB)


Soft result done in Rawtherapee but probably a good starting point for gimp :slight_smile:

DSC00989b.pp3 (12.6 KB)

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Your photo is an excellent test for dehaze methods, thanks @Dario. Here I used RawTherapee 5.3-dev tone mapping, plus the new® so-called HDR tone mapping (which is based on the Fattal code from LuminanceHDR), and Retinex to help beat the fog, and adjusted some color curves. Inverse gradient on the sky to illuminate the clouds.DSC00989.ARW.pp3 (14.0 KB)


My B&W take.

DSC00989-3.ARW-1.jpg.out.pp3 (10.8 KB)


Here are two more variations, using ImageMagick, from Dario’s hazy-blue original photo. I haven’t sharpened them.

First, simply equalize the colour channels, with the same transformation to each:

convert salvage.jpg -equalize salvage_equ.png

Next, apply equalization to each channel independently:

convert salvage.jpg -channel RGB -equalize +channel salvage_equrgb.png

Equalizing is a powerful tool to help us see what an image contains, like a “proof print” from a negative. It may give a lousy image aesthetically, but this one is good.