The subject of the thesis is to explore the properties of the adaptive nonlinear filter in the problem of signal and image recovery from indirect (incomplete and blurry) observations.
The objective of this work is twofold. First, the statistical properties of the proposed algorithms should be studied under various observation and signal scenarios. Second, we aim to devise fast adaptive filter implementation through iterative saddle-point optimization which allows to treat efficiently large-scale data.