Abstract: Image demosaicing is described, for example, to enable raw image sensor data, where image elements have intensity values in only one of three color channels, to be converted into a color image where image elements have intensity values in three color channels. In various embodiments a trained machine learning component is used to carry out demosaicing optionally in combination with denoising. In some examples the trained machine learning system comprises a cascade of trained regression tree fields. In some examples the machine learning component has been trained using pairs of mosaiced and demosaiced images where the demosaiced images have been obtained by downscaling natural color digital images. For example, the mosaiced images are obtained from the demosaiced images by subsampling according to one of a variety of color filter array patterns.

Abstract: A new tractable model solves labeling problems using regression tree fields, which represent non-parametric Gaussian conditional random fields. Regression tree fields are parameterized by non-parametric regression trees, allowing universal specification of interactions between image observations and variables. The new model uses regression trees corresponding to various factors to map dataset content (e.g., image content) to a set of parameters used to define the potential functions in the model. Some factors define relationships among multiple variable nodes. Further, the training of regression trees is scalable, both in the training set size and in the fact that the training can be parallelized. In one implementation, maximum pseudolikelihood learning provides for joint training of various aspects of the model, including feature test selection and ordering (i.e., the structure of the regression trees), parameters of each factor in the graph, and the scope of the interacting variable nodes used in the graph.

Abstract: A new tractable model solves labeling problems using regression tree fields, which represent non-parametric Gaussian conditional random fields. Regression tree fields are parameterized by non-parametric regression trees, allowing universal specification of interactions between image observations and variables. The new model uses regression trees corresponding to various factors to map dataset content (e.g., image content) to a set of parameters used to define the potential functions in the model. Some factors define relationships among multiple variable nodes. Further, the training of regression trees is scalable, both in the training set size and in the fact that the training can be parallelized. In one implementation, maximum pseudolikelihood learning provides for joint training of various aspects of the model, including feature test selection and ordering (i.e., the structure of the regression trees), parameters of each factor in the graph, and the scope of the interacting variable nodes used in the graph.