Abstract: Imaging processing techniques using a trained convolution neural network (CNN) are described. In one or more implementations, an image processing system and method are provided for applying an image processing algorithm to a dataset of training images to generate a plurality of performance curves, constructing a loss function based upon the plurality of performance curves, training a convolutional neural network (CNN) to optimize the loss function to establish a trained convolutional neural network, predicting a specific tuning parameter for an image of interest using the trained convolution neural network and performing image processing of the image of interest using the specific tuning parameter and the image processing algorithm to generate a processed image of interest.

Abstract: The present invention provides a system and method for generating a dataset of uncompressed color images taken with three digital cameras and exhibiting different levels of real noise, resulting from low-light conditions. For each scene there are, on average, two low-noise and two noisy images that are aligned at the pixel level both spatially and in intensity. In one embodiment, the dataset contains over 100 scenes and more than 400 images, including both 16-bit RAW formatted images and 8-bit BMP pixel and intensity aligned images from three digital cameras. The generated dataset of real noise images is useful in the evaluation of denoising algorithms.