1 August 1991 Massively parallel image restoration
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An image restoration method that performs piecewise smooth restorations on images corrupted with very high noise levels is presented. The restoration is based on a Markov random field (MRF) model using a neural network sigmoid nonlinearity between pixels to produce a restoration with sharp boundaries while providing noise reduction. The model is currently implemented using an efficient deterministic search algorithm that typically requires less than 200 iterations in a digital computer simulation. The algorithm is able to restore images with up to 71% of the pixels corrupted with sensor noise (non-gaussian). Results from simulation indicate that the MRF restoration is capable of operating at signal-to-noise ratios 5 to 6 dB lower than the median filtering. The same model can be applied to a large number of sensor measurements (Doppler, intensity, passive infrared, range, and video sensors) by simply adjusting a single parameter. This is especially relevant for hardware implementation, since a single chip can be used for processing a wide variety of imagery. The model has a massively parallel architecture with local neighbor interactions (four nearest neighbors), and can be implemented on a large parallel computer or a custom analog VLSI chip. Implementation of the model in analog VLSI would allow video rate restoration of 512 X 512 pixel images.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Murali M. Menon, Murali M. Menon, "Massively parallel image restoration", Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); doi: 10.1117/12.44877; https://doi.org/10.1117/12.44877


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