BLADE: Filter Learning for General Purpose Computational Photography
This work provides a general and efficient solution for various computational photography tasks, but it is incremental as it builds upon the existing RAISR method.
The authors tackled the problem of general-purpose computational photography by generalizing the RAISR method into BLADE, a trainable edge-adaptive filtering framework, and demonstrated its applications in denoising, demosaicing, and stylization with computational efficiency.
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.