Learned Perceptual Image Enhancement
This work addresses the need for more perceptually compelling results in image enhancement for applications like photography and computer vision, though it is incremental as it builds on existing learning frameworks.
The paper tackles the problem of improving perceptual quality in learned image enhancement by adding a learned no-reference image quality metric to the loss function, which significantly enhances operators like local tone mapping and dehazing without extra inference complexity.
Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is implemented using a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preferences of human raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing.