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Filtering has been one of the main approaches to texture analysis since early on. Traditionally, the process involved designing the filters essentially by hand based on some prior knowledge (e.g. perceptual models, optimal mathematical properties, etc.) In this work we propose the use of convolutional networks for refactoring traditional, hand-designed filters. Our method consists of initialising the first convolutional layer of the network with some classic banks of filters, training the network on texture images and retrieve the modified filters. Experimenting with five classes of filters and eight datasets of texture images we show that the refactored filters can be conveniently used `off-the-shelf' to achieve better performance than obtained with the original filters, but at the same computational cost.