A robust technique for image descreening based on the wavelettransform
IEEE Transactions on Signal Processing
On the stability of sigma delta modulators
IEEE Transactions on Signal Processing
A fast, high-quality inverse halftoning algorithm for error diffused halftones
IEEE Transactions on Image Processing
Halftone to continuous-tone conversion of error-diffusion coded images
IEEE Transactions on Image Processing
Inverse halftoning and kernel estimation for error diffusion
IEEE Transactions on Image Processing
The sigma-delta CNN with second order noise shaping property
WSEAS Transactions on Circuits and Systems
A second order Σ Δ modulation by cascaded Σ Δ CNNs
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
Synchronization for a class of uncertain chaotic cellular neural networks with time-varying delay
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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Although sigma-delta modulation is widely used for analog-to-digital (A/D) converters, sigma-delta concepts are only for 1D signals. Signal processing in the digital domain is extremely useful for 2D signals such as used in image processing, medical imaging, ultrasound imaging, and so on. The intricate task that provides true 2D sigma-delta modulation is feasible in the spatial domain sigma-delta modulation using the discrete-time cellular neural network (DT-CNN) with a C-template. In the proposed architecture, the A-template is used for a digital-to-analog converter (DAC), the C-template works as an integrator, and the nonlinear output function is used for the bilevel output. In addition, due to the cellular neural network (CNN) characteristics, each pixel of an image corresponds to a cell of a CNN, and each cell is connected spatially by the A-template. Therefore, the proposed system can be thought of as a very large-scale and super-parallel sigma-delta modulator. Moreover, the spatio-temporal dynamics is designed to obtain an optimal reconstruction signal. The experimental results show the excellent reconstruction performance and capabilities of the CNN as a sigma-delta modulator.