Controlled Feature Adjustment for Image Processing and Synthesis

Feature adjustment, understood as the set of techniques aimed at modifying at will global features of given signals, has cardinal importance for several signal processing applications, such as enhancement, restoration, style transfer, and synthesis. Despite of this, it has not yet been approached from a general, theory-grounded, perspective. This work proposes a new conceptual and practical methodology that we term Controlled Feature Adjustment (CFA). CFA provides methods for, given a set of parametric global features (scalar functions of discrete signals), (1) constructing a related set of deterministically decoupled features, and (2) adjusting these new features in a controlled way, i.e., each one independently of the others. We illustrate the application of CFA by devising a spectrally-based hierarchically decoupled feature set and applying it to obtain different types of image synthesis that are not achievable using traditional (coupled) feature sets.