Total p-norm Variation (TpV) is a well-established technique in image processing, used to denoise and preserve edges. However, the related non-convex minimization is still a challenging task in optimization, both for the computational cost and for convergence guarantees toward a good local minimum. We propose a framework, called (TpV){\$}{\$}^2{\$}{\$}2, embedding a convolutional neural network to speed up the iterative reconstruction while preserving converging features. The resulting hybrid method is robust and accurate. Verifications and comparisons illustrate that the proposed method is effective and promising.
Morotti, E., Evangelista, D., Loli Piccolomini, E. (2025). Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing. Cham : Springer Nature Switzerland [10.1007/978-3-031-81241-5_30].
Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing
Morotti, Elena
Primo
;Evangelista, DavideSecondo
;Loli Piccolomini, ElenaUltimo
2025
Abstract
Total p-norm Variation (TpV) is a well-established technique in image processing, used to denoise and preserve edges. However, the related non-convex minimization is still a challenging task in optimization, both for the computational cost and for convergence guarantees toward a good local minimum. We propose a framework, called (TpV){\$}{\$}^2{\$}{\$}2, embedding a convolutional neural network to speed up the iterative reconstruction while preserving converging features. The resulting hybrid method is robust and accurate. Verifications and comparisons illustrate that the proposed method is effective and promising.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
