System fusion in passive sensing using a modified hopfield network

Resolution-enhanced radar/SAR imaging: an experiment design framework combined with neural network-adapted variational analysis regularization

“…These features of the POCS-regularized DEDR-VArelated algorithms generalized by (16) result in the drastically decreased algorithmic computational complexity (e.g., up to~10 3 times for the typical large-scale 10 3 × 10 3 SAR pixel image formats [8]). Next, several RS images formed by different sensor systems or applying different image formation techniques can be aggregated into an enhanced fused RS image employing the NN computational framework [10]. We are now ready to proceed with construction of such NN-adapted DEDR-VA-related techniques.…”

“…Define the discrepancies between the actually formed images {q (p) } and the true original image b as the l 2 squired norms, J p (b) = ||q (p) -F (p) b|| 2 ; p = 1,...,P. Let us next adopt the VA inspired proposition [10] that the smoothness properties of the desired image are controlled by the second-order Tikhonov stabilizer, J P+1 (b) = b T P 1 b , where…”

“…T represents the vector of weight parameters, commonly referred to as the fusion regularization parameters [10]. Hence, in the frame of the aggregate regularization approach to decentralized fusion [2,6], the restored image is to be found as a solution of the convex optimization problem…”

“…for the assigned values of the regularization parameters l. A proper selection of l is next associated with parametrical optimization [10] of such the aggregated fusion process.…”

Multispectral image fusion for improved RGB representation based on perceptual attributes

“…Pixel-level fusion techniques can also be used to improve the efficiency of classification and detection algorithms. In general, pixel-level fusion methods can be classified into linear methods (Achalakul and Taylor 2001), nonlinear methods (Matsopoulos et al 1994, Matsopoulos and Marshall 1995, Mukhopadhyay and Chanda 2001, optimization techniques (Solberg et al 1996), neural networks (Zhang et al 2001, Shkvarko et al 2001) and image pyramids (Liu et al 2001).…”