A nonlinear image restoration framework using vector quantization

Vector quantization (VQ) is a powerful method used primarily in signal and image compression. In recent years, it has also been applied to various other image processing tasks, including image classification, histogram modification, and restoration. In this paper, we focus our attention on image restoration using VQ. We present a general framework that incorporates two other methods in the literature, and discuss our method that follows more naturally from this framework. With appropriate training data for the VQ codebook, this method can restore images beyond its diffraction limit. © 2004 IEEE.published_or_final_versio

BLADE: Filter Learning for General Purpose Computational Photography

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