Hi Andy,

This warning tells you that you're going too far with deconvolution

The regularized deconvolution algorithms that we have implemented in the Deconvolution tool are

*globally convergent*. This means that after a sufficiently large number of iterations, the algorithms converge to a

*stable solution*. Once this point has been reached, performing more iterations does not improve the deconvolved result.

If you perform more deconvolution iterations beyond convergence, the algorithms usually become

*locally divergent*. When this happens, the signal/noise ratio starts getting worse and better successively, and the neat result is that no further improvement can be achieved.

Code:

```
Iteration 77/100: 100%
*** Warning: local divergence at iteration #77. Accumulated divergence: 0.253597
sigma=0.00997724 delta=-0.00014327 noise=0.00069897 significant=0.15275]
```

In this example, you probably should stop at about 70 or 75 iterations. 100 iterations won't hurt your image, but you'll be basically wasting time to no avail.

The information given is as follows:

**sigma**
The standard deviation of the image after the last deconvolution iteration. This value should decrease between successive iterations. If it increases, then the procedure is locally divergent.

**delta**
The ratio of improvement in standard deviation between the previous and current iterations. This value is equal to:

delta = (sigma_prev - sigma)/sigma

and should always be positive. If it is negative, then the procedure is locally divergent.

**noise**
The estimated standard deviation of the noise (either assuming a Gaussian or a Poisson noise model, depending on your choice in regularization parameters). This value should decrease between successive iterations.

**significant**
This measures the amount of significant structures in the image. Significant structures are what we are trying to improve with regularized deconvolution. This number should decrease between successive iterations.

I completed

another mosaic a few weeks ago and had this problem then but it went away when I cropped the image inside the blocks of the mosaic. This time, that is not practical. The image would lose too much if it were cropped.

Nice mosaic. Did you use Dynamic Range Extension (high range) to prevent saturation? I definitely prefer the first version (more contrasted).

This time, I made the background 100% black, hoping to avoid the problem, but there it is. Would white be better?

An important problem with mosaics are steep transitions at block edges, which cause strong ringing problems. Ringing can fool the regularization algorithms, which may prevent you from applying deconvolution correctly. A white background can be even worse, since then dark rings will occur over the image. You have two possible solutions:

- Blur all mosaic block edges reasonably, and use a black background. This will prevent steep transitions that cause ringing.

- Use a neutral gray background, as similar as possible to the mean gray level of the image. You can also blur edges in this case.

I'll post raw data if that will help

I can take a look if you want. My schedule is quite full, though, so I'll be slow. The mosaic problem is interesting, and I'm thinking in a script to automatically blur block edges.

P.S., Congratulations to Vincent Peris on the APOD of the Sombrero Galaxy!

He's out of town now but sure will be happy to read this. Congratulations again Vicent!

Cheers,

Juan