Show-through cancellation in scanned images using blind source separation techniques

ABSTRACT Show-Through is a common occurrence when scanning duplex printed documents. The back-side printing shows through the paper, contaminating the front side image. Previous work modeled the problem as a non-linear convolutive mixture of images and offered solutions based on decorrelation. In this work we propose a cleaning process based on a Blind Source Separation approach. We define a cost function incorporating the non-linear mixing model in a mean-squared error term, along with a regularization term based on Total-Variation. We propose a location dependent regularization tradeoff, preserving image edges while removing show-through edges. The images and mixing parameters are estimated using an alternating minimization process, with each stage using only convex optimization methods. The resulting images exhibit significantly lower show-through, both visibly and in objective measures

Multi-Scale Dictionary Learning using Wavelets

International audienc

Sequential Minimal Eigenvalues - An Approach to Analysis Dictionary Learning

International audienceOver the past decade there has been a great interest in asynthesis-based model for signals, based on sparse and re-dundant representations. Such a model assumes that the sig-nal of interest can be decomposed as a linear combinationof few columns from a given matrix (the dictionary). An al-ternative, analysis-based, model can be envisioned, where ananalysis operator multiplies the signal, leading to a sparseoutcome. In this paper we propose a simple but effectiveanalysis operator learning algorithm, where analysis "atoms"are learned sequentially by identifying directions that are or-thogonal to a subset of the training data. We demonstratethe effectiveness of the algorithm in three experiments, treat-ing synthetic data and real images, showing a successful andmeaningful recovery of the analysis operator

Sequential Minimal Eigenvalues - An Approach to Analysis Dictionary Learning

International audienceOver the past decade there has been a great interest in asynthesis-based model for signals, based on sparse and re-dundant representations. Such a model assumes that the sig-nal of interest can be decomposed as a linear combinationof few columns from a given matrix (the dictionary). An al-ternative, analysis-based, model can be envisioned, where ananalysis operator multiplies the signal, leading to a sparseoutcome. In this paper we propose a simple but effectiveanalysis operator learning algorithm, where analysis "atoms"are learned sequentially by identifying directions that are or-thogonal to a subset of the training data. We demonstratethe effectiveness of the algorithm in three experiments, treat-ing synthetic data and real images, showing a successful andmeaningful recovery of the analysis operator