Image Processing can
be applied for one of two basic
reasons, image restoration or image enhancement. The goal of
restoration
is to achieve an accurate depiction of the scene being imaged. In
contrast, enhancement strives to create the most visually appealing
image. An example of image enhancement is to slightly blur an
image to reduce the amount of visible noise.
There is a gray area
in this definition. An analyst might apply an edge detector to
make it easier to count interference fringes even though the processed
image is less accurate than the original. The image itself
might be less accurate, however the information the analyst gathers
from the image is more accurate.
Deconvolution is used to achieve either or both of these
objectives. Blur removal typically makes the image more visually
pleasing while creating a replication that is more accurate.
Blind deconvolution is also a very useful image enhancement tool; its
use in
restoration comes with a caveat. How can you verify the accuracy
of the
method if some portion of the input is based on a guess?
Aside from special situations, generally the answer is that you cannot
directly
determine the accuracy of your result. So does this uncertainty
preclude
the use of blind deconvolution for image restoration? Based on
the
increasing amount of scientific applications that use blind
deconvolutions,
this answer is no.
The reasons are:
1.
In
order to produce a positive result, a very good guess is
required. For iterative
techniques, poor input produces non-converging results, i.e. the
algorithm does
not realize it is straying further and further away from the right
answer. Poor input into non-iterative methods produces results
that are
dominated by image artifacts.
2.
Blind
deconvolutions are mostly applid to images where the blur function is
space-invariant. This means that we expect to see the same blur
function
occur at any point in the image. If artifacts do occur, they are
not
isolated events within the image. The analyst will quickly
identify
non-real objects in most images. Edge ringing is the most common
artifact
that occurs, and this can found using either qualitative or
quantitative
methods.
3.
Suspect
features in a processed image can also be compared to the original
image.
Although blurred, these features, if real, must be in the original
image.
Except in cases of extreme blur, the analyst will easily locate the
feature in
the original image.
4.
The image analyst has
a good understanding of
the imaging system and the object that the camera is imaging. For example, planetary probes, such as
NASA's Cassini, send back images of places we have never seen before. Even though he/she possesses little
knowledge of the planet or moon, the analyst expects to see a landscape
that
has at least similar characteristics to those that he has seen before. The analyst then draws on his knowledge of
previous landscapes to process the new images. The
human mind sees very accurate depictions
of scenes all the time, and
is well practiced by nature in the art of determining image quality. It is this knowledge that provides an
additional quantitative improvement to image restoration.
The utility of
SeDDaRA for image restoration depends on
these qualities. The analyst is asked to choose a reference image
that
resembles the spatial frequency content of the original, process the
image with
a few chosen parameters, and then evaluate the result. In absence
of
standard image metrics, the analyst determines whether the result is
adequate
or needs further tuning. However, the speed of
SeDDaRA enables the
analyst to perform this task in a few minutes as opposed to hours.
On this page, we have
expressed our opinion on the use
of blind deconvolution for image restoration. We are obviously in
favor
of it. However, others may not be. If you have a concern
about how
this argument was presented or about the content, please let us
know.
