Image capturing by CCD/CMOS cameras is encumbered with two fundamental perturbing influences. Time invariant blurring
(image convolution with fixed kernel) and time variant noises. Both of these influences can be successfully eliminated
by the iterative detection networks (IDNs), that effectively and suboptimally (iteratively) solve 2D MAP criterion through
the image decomposition to the small areas. Preferably to the individual pixel level, if this allows the noise distribution (statistically
independent noise). Nevertheless, this task is so extremely numerically exacting and therefore the contemporary
IDNs are limited only for restorations of dichromatic images.
The IDNs are composed of certain, as simple as possible, statistical devices (SISO modules) and can be separated into two
basic groups with variable topology (exactly matched to the blurring kernel) and with fixed topology, same for all possible
kernels. The paper deals with second group of IDNs, concretely with IDNs whose SISO modules are concatenated in
three directions (horizontal, vertical and diagonal). Advantages of such ordering rests in the application flexibility (can be
comfortable applied to many irregular cores) and also in the low exigencies to number of memory devices it the IDN. The
mentioned IDN type will be implemented in the two different variants suppressing defocusing in the lens of CCD/CMOS
sensing system and will be verified in the sphere of a dichromatic 2D barcode detection.
The paper deals with elimination of defocusing and thermal noise out of black & white pictures captured by a
CCD/CMOS camera with imperfectly adjusted lens. For purposes of image recovery we can use the MAP criterion based
iterative detection network (IDN) containing a number of mutually concatenated functional blocks so-called soft
inversions (SISOs). This cellular structure makes IDN suboptimal but also numerically very simple and practically
applicable in contrast to an unviable optimal (single-stage) MAP detector. Firstly we focus closer on SISO entities and
consequently on the creation of entire IDN, specifically the so-called distributed IDN marginalizing at the symbol level.
In the end, image reconstruction example will be presented (using this type of IDN) along with its performance
characteristics (BER curves) for various levels of defocus.
The paper deals with elimination of blurring caused by the object moving and thermal noise in black & white pictures captured by a CCD/CMOS camera. This problem can be also interpreted like image passage through some kind of ISI channel with specific 2D impulse response. Hence for purposes of image recovery we can use the MAP criterion based iterative detection network (IDN) containing a number of mutually concatenated functional blocks so-called soft inversions (SISOs). This cellular structure makes IDN suboptimal but also numerically very simple and practically applicable in contrast to an unviable optimal (single-stage) MAP detector. Firstly we focus closer to parameters determination of the image blurring hypothetical model (misrepresenting ISI channel). Consequently we are going to deal with the SISO entities and the synthesis of entire IDN, specifically the synthesis of so-called distributed IDN marginalizing at the symbol level because this structure presents the best solution for the mentioned issue. At the end, the image reconstruction example will be presented (using this type of IDN).
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