Research on automated image enhancement has gained momentum in recent years, partially due to the need for easy-to-use tools for enhancing pictures captured by ubiquitous cameras on mobile devices. Many of the existing leading methods employ machine-learning-based techniques, by which some enhancement parameters for a given image are found by relating the image to the training images with known enhancement parameters. While knowing the structure of the parameter space can facilitate search for the optimal solution, none of the existing methods has explicitly modeled and learned that structure. This paper presents an end-to-end, novel joint regression and ranking approach to model the interaction between desired enhancement parameters and images to be processed, employing a Gaussian process (GP). GP allows searching for ideal parameters using only the image features. The model naturally leads to a ranking technique for comparing images in the induced feature space. Comparative evaluation using the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on an additional data-set were used to demonstrate the effectiveness of the proposed approach.
Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Because of these characteristics, existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep machine learning has shown unique abilities to address hard problems that resisted machine algorithms for long. This motivated us to explore the use of deep learning in the context of photo editing. In this paper, we explain how to formulate the automatic photo adjustment problem in a way suitable for this approach. We also introduce an image descriptor that accounts for the local semantics of an image. Our experiments demonstrate that our deep learning formulation applied using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on the image semantics. We show on several examples that this yields results that are qualitatively and quantitatively better than previous work.  Automatic photo enhancement is one of the long-standing goals in image processing and computational photography. While a variety of methods have been proposed for manipulating tone and colour, most automatic methods used in practice, operate on the entire image without attempting to take the content of the image into account. In this paper, we present a new framework for automatic photo enhancement that attempts to take local and global image semantics into account. Specifically, our content-aware scheme attempts to detect and enhance the appearance of human faces, blue skies with or without clouds and underexposed salient regions. A user study was conducted that demonstrates the effectiveness of the proposed approach compared to existing auto-enhancement tools. © 2012 Wiley Periodicals, Inc. We present a framework for generating content-adaptive macros that can transfer complex photo manipulations to new target images. We demonstrate applications of our framework to face, landscape, and global manipulations. To create a content-adaptive macro, we make use of multiple training demonstrations. Specifically, we use automated image labeling and machine learning techniques to learn the dependencies between image features and the parameters of each selection, brush stroke, and image processing operation in the macro. Although our approach is limited to learning manipulations where there is a direct dependency between image features and operation parameters, we show that our framework is able to learn a large class of the most commonly used manipulations using as few as 20 training demonstrations. Our framework also provides interactive controls to help macro authors and users generate training demonstrations and correct errors due to incorrect labeling or poor parameter estimation. We ask viewers to compare images generated using our content-adaptive macros with and without corrections to manually generated ground-truth images and find that they consistently rate both our automatic and corrected results as close in appearance to the ground truth. We also evaluate the utility of our proposed macro generation workflow via a small informal lab study with professional photographers. The study suggests that our workflow is effective and practical in the context of real-world photo editing.