The concept of image regularization attempts to further refine or recover the HR image that is otherwise not covered properly by the Shift-Add fusion technique [4].
The proof essentially uses D. Z. Arov′s concept of A-regular and A-singular j-inner matrix-functions and is based on an analysis of V. Katsnelson′s results on their regularization.
For this goal, we use the concept of the Filippov-like regularization (see [15]) of spatially discontinuous maps.
The unified approach that we address to solve such a problem is based on the recently proposed concept of descriptive experiment design regularization (DEDR) [7, 8].
A new aggregated Hardware/Software (HW/SW) codesign approach to optimization of the digital signal processing techniques for enhanced imaging with real-world uncertain remote sensing (RS) data based on the concept of descriptive experiment design regularization (DEDR) is addressed.
For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization.
Different norms have different effects of regularization.
For different types of regularization, we establish energy estimates.
This point is illustrated using the Bayesian interpretation of regularization.
It was the first of a series of "regularization programs" that continued over the following decade.
