Calculating the SNR of a blurred image
Estimating the noise power is difficult because it involves separating the signal from the noise.

A good way to proceed is to take a measurement where you know the correct answer, and subtract. For example, you could photograph a grey rectangle, and look for pixels that are not the right level of grey.

If you only have the one image, you need to be a bit more imaginative. Maybe there is a patch of sky, or something, which you suspect to be very simple. Then you can form a mathematical model of the signal in that region, and fit its parameters to the image. For example, if you think the colour is constant in the region, you can estimate it by averaging over the region. Or if you think the region is a long way out of focus, you can estimate the signal using a low-pass filter. The goal is to obtain a low-entropy estimate of the true signal. Then you can subtract it off to obtain an estimate of the noise.

Note that the noise level and the signal level can both vary across an image. Cheap digital cameras have a noise level that depends on the brightness of the pixel, due to the way the detectors work, and also on the position of the pixel, due to in-camera compensation of optical artifacts. You might therefore want to estimate the noise using a variety of different patches of the image.