  Energy reconstruction in calorimeters operating in high luminosity particle
colliders has become a remarkable challenge. In this scenario, pulses from a
calorimeter front-end output overlap to each other (pile-up effect),
compromising the energy estimation procedure when no preprocessing for signal
disentanglement is accomplished. Recently, methods based on signal
deconvolution have been proposed for both online and offline reconstructions.
For online processing, constraints concerning fast processing, memory
requirements, and cost implementation limit the overall performance. Offline
reconstruction allows the use of Sparse Representation (SR) theory to implement
sophisticated Iterative Deconvolution (ID) methods. This paper presents ID
methods based on SR algorithms whose computational cost is effective for online
implementation. Using simulation data, current techniques were compared to the
proposed SR ones for performance validation in the online environments.
Analysis has shown that, despite the higher computational cost, when compared
to standard methods, the increase in performance may justify the use of the
proposed techniques, in particular for the Separable Surrogate Functional
(SSF). which is shown to be feasible for implementation in modern FPGAs.
