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Many image transformations can be modeled by image operators that arecharacterized by pixel-wise local functions defined on a finite support window.In image operator learning, these functions are estimated from training datausing machine learning techniques. Input size is usually a critical issue whenusing learning algorithms, and it limits the size of practicable windows. Wepropose the use of convolutional neural networks (CNNs) to overcome thislimitation. The problem of removing staff-lines in music score images is chosento evaluate the effects of window and convolutional mask sizes on the learnedimage operator performance. Results show that the CNN based solutionoutperforms previous ones obtained using conventional learning algorithms orheuristic algorithms, indicating the potential of CNNs as base classifiers inimage operator learning. The implementations will be made available on theTRIOSlib project site.

Image operator learning coupled with CNN classification and its application to staff line removal