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. 2008 Sep 26;8(9):6108-6124.
doi: 10.3390/s8096108.

Spectral-Based Blind Image Restoration Method for Thin TOMBO Imagers

Amar A El-Sallam  1 , Farid Boussaid  2
Affiliations

Spectral-Based Blind Image Restoration Method for Thin TOMBO Imagers

Amar A El-Sallam et al. Sensors (Basel). .

Abstract

With the recent advances in microelectronic fabrication technology, it becomes now possible to fabricate thin imagers, less than half a millimeter thick. Dubbed TOMBO (an acronym for thin observation module by bound optics), a thin camera-on-a-chip integrates micro-optics and photo-sensing elements, together with advanced processing circuitry, all on a single silicon chip. Modeled after the compound-eye found in insects and many other arthropods, the TOMBO imager captures simultaneously a mosaic of low resolution images. In this paper, we describe and analyze a novel spectral-based blind algorithm that enables the restoration of a high resolution image from the captured low resolution images.The proposed blind restoration method does not require prior information about the imaging system nor the original scene. Furthermore, it alleviates the need for conventional de-shading and rearrangement processing techniques. Experimental results demonstrate that the proposed method can restore images for SNER lower than 3dB.

Keywords: Back-Projection; CMOS Imager; Cross-correlation; Image Restoration; Spectra; TOMBO.

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Figures

Figure 1.
Figure 1.
The TOMBO architecture
Figure 2.
Figure 2.
Cross correlation-based pixel rearrange method
Figure 3.
Figure 3.
A model for the shading and de-shading pre-processing step
Figure 4.
Figure 4.
A mathematical model for the TOMBO system
Figure 5.
Figure 5.
A spectral representation for the captured images in the TOMBO system.
Figure 6.
Figure 6.
A spectral diagram showing how cross-spectra are estimated in the case of a 1-d signal
Figure 7.
Figure 7.
Blind Image Restoration Algorithm
Figure 8.
Figure 8.
Experimental, no added noise, 6 × 6 lenses
Figure 9.
Figure 9.
Experimental, External noise, 6 × 6 unit images
Figure 10.
Figure 10.
Experimental, Internal and external noise, 6 × 6 unit images
Figure 11.
Figure 11.
Experimental, External correlated noise, 6 × 6 unit images
Figure 12.
Figure 12.
Simulation, no added noise, 4 × 4 unit images
See this image and copyright information in PMC

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