Abstract: A method, system and computer program product for improving accuracy and computation efficiency in interpolation, upsampling and color channel estimation. A Bayesian estimator used to estimate the value of a pixel in an image is constructed using measurements of high-order (e.g., 3rd, 4th, 5th) statics for nearby points in natural images. These measurements reveal highly systematic statistical regularities that were ignored from the prior algorithms due to their restrictive measurements and assumptions. As a result, the accuracy in interpolation, upsampling and color channel prediction is improved. Furthermore, the process for constructing a Bayesian estimator is simpler and more direct by storing in a table the mean value of the pixel value to be estimated for each combination of values of nearby points in training samples. As a result of having a simpler and more direct approach than existing methods, the computational efficiency is improved.

Abstract: Methods and composition for denoising digital camera images are provided herein. The method is based on directly measuring the local statistical structure of natural images in a large training set that has been corrupted with noise mimicking digital camera noise. The measured statistics are conditional means of the ground truth pixel value given a local context of input pixels. Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context. The conditional means are measured and applied recursively (e.g., the second conditional mean is measured after denoising with the first conditional mean). Each local context vector consists of only three variables, and hence the conditional means can be measured directly without prior assumptions about the underlying probability distributions, and they can be stored in fixed lookup tables.

Abstract: Methods and composition for denoising digital camera images are provided herein. The method is based on directly measuring the local statistical structure of natural images in a large training set that has been corrupted with noise mimicking digital camera noise. The measured statistics are conditional means of the ground truth pixel value given a local context of input pixels. Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context. The conditional means are measured and applied recursively (e.g., the second conditional mean is measured after denoising with the first conditional mean). Each local context vector consists of only three variables, and hence the conditional means can be measured directly without prior assumptions about the underlying probability distributions, and they can be stored in fixed lookup tables.

Abstract: A method, system and computer program product for improving accuracy and computation efficiency in interpolation, upsampling and color channel estimation. A Bayesian estimator used to estimate the value of a pixel in an image is constructed using measurements of high-order (e.g., 3rd, 4th, 5th) statics for nearby points in natural images. These measurements reveal highly systematic statistical regularities that were ignored from the prior algorithms due to their restrictive measurements and assumptions. As a result, the accuracy in interpolation, upsampling and color channel prediction is improved. Furthermore, the process for constructing a Bayesian estimator is simpler and more direct by storing in a table the mean value of the pixel value to be estimated for each combination of values of nearby points in training samples. As a result of having a simpler and more direct approach than existing methods, the computational efficiency is improved.