Optimizing the Parameters for Post-processing Consumer Photos via Machine Learning

Photo sharing in social media is a part of everyday life for many, as inexpensive cameras integrated in smartphones are widely available. Unfortunately, low cost consumer devices are often prone to capture artifacts, and this is why there is a growing demand for automatic post-processing to enhance the image quality. Due to the wide range of distortions in nonprofessional photography, automatic selection of the post-processing methods and parameters is a challenging problem. In this paper, we present a subjective study based on rank-ordering method, comparing the subjective preferences between photos processed with different parameters for image sharpening and denoising. The subjective results are used as a basis to derive the ground truth values for the post-processing parameters for different photos. Then, we apply a pre-trained convolutional neural network (CNN) to extract a set of features from photos, used as input to a regression model to predict the optimal post-processing parameters. Test results show that the learning-based approach can predict post-processing parameters with a satisfactory accuracy.