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Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification
Jose Carranza-Rojas, Saul Calderon-Ramirez, Adán Mora-Fallas, Michael Granados-Menani, Jordina Torrents-Barrena
Image enhancement refers to the enrichment of certain image features such as edges, boundaries, or contrast. The main objective is to process the original image so that the overall performance of visualization, classification and segmentation tasks is considerably improved. Traditional techniques require manual fine-tuning of the parameters to control enhancement behavior. To date, recent Convolutional Neural Network (CNN) approaches frequently employ the aforementioned techniques as an enriched pre-processing step. In this work, we present the first intrinsic CNN pre-processing layer based on the well-known unsharp masking algorithm. The proposed layer injects prior knowledge about how to enhance the image, by adding high frequency information to the input, to subsequently emphasize meaningful image features. The layer optimizes the unsharp masking parameters during model training, without any manual intervention. We evaluate the network performance and impact on two applications: CIFAR100 image classification, and the PlantCLEF identification challenge. Results obtained show a significant improvement over popular CNNs, yielding 9.49% and 2.42% for PlantCLEF and general-purpose CIFAR100, respectively. The design of an unsharp enhancement layer plainly boosts the accuracy with negligible performance cost on simple CNN models, as prior knowledge is directly injected to improve its robustness.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-100 | ResNet20+UnsharpMaskLayer | Percentage correct | 60.36 | #197 of 211 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | USM (COCO TS + ICDAR–2013) | F-Measure | 80.40% | #13 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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