# BLADE: Filter Learning for General Purpose Computational Photography

arXiv: https://arxiv.org/abs/1711.10700 · HTML: https://mlsanity.com/paper/1711.10700/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 12 of 12)

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- [Fast and High-Quality Image Denoising via Malleable Convolutions](https://mlsanity.com/paper/2201.00392/) (2022)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
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- [Procedural Kernel Networks](https://mlsanity.com/paper/2112.09318/) (2021)
- [Solving Image PDEs with a Shallow Network](https://mlsanity.com/paper/2110.08327/) (2021)
- [LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation](https://mlsanity.com/paper/2407.09935/) (2024)
- [A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU](https://mlsanity.com/paper/2303.08999/) (2023)
- [A Dictionary Based Approach for Removing Out-of-Focus Blur](https://mlsanity.com/paper/2406.11330/) (2024)

## Abstract (arXiv)

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization.