BLADE: Filter Learning for General Purpose Image Processing
Abstract
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 image processing
problems. We show applications to denoising, compression artifact removal,
demosaicing, and approximation of anisotropic diffusion equations.
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 image processing
problems. We show applications to denoising, compression artifact removal,
demosaicing, and approximation of anisotropic diffusion equations.
Research Areas
