Smallsat constellations are an increasingly common source of global-scale overhead imagery that are refreshed with a higher frequency than traditional satellites. The smaller size and lower cost of smallsats enable frequent revisits, but result in images with lower resolution and quality than the high resolution (HR) images from traditional satellites. In order to benefit from the increased temporal frequency provided by smallsat constellations, new approaches are needed to automatically detect objects in their imagery. We present a super resolution (SR) approach that incorporates domain adaptation (DA) to enable object detection from low resolution (LR) images without the need for paired training data or annotations in the LR domain. Our Resolution Transfer approach addresses the resolution and quality loss for smallsats, as demonstrated via airplane detection.

What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algorithms as a pre-processing step improve image interpretability for manual analysis or automatic visual recognition to classify scene content? While there have been important advances in the area of computational photography to restore or enhance the visual quality of an image, the capabilities of such techniques have not always translated in a useful way to visual recognition tasks. Consequently, there is a pressing need for the development of algorithms that are designed for the joint problem of improving visual appearance and recognition, which will be an enabling factor for the deployment of visual recognition tools in many real-world scenarios. To address this, we introduce the UG$^2$dataset as a large-scale benchmark composed of video imagery captured under challenging conditions, and two enhancement tasks designed to test algorithmic impact on visual quality and automatic object recognition. Furthermore, we propose a set of metrics to evaluate the joint improvement of such tasks as well as individual algorithmic advances, including a novel psychophysics-based evaluation regime for human assessment and a realistic set of quantitative measures for object recognition performance. We introduce six new algorithms for image restoration or enhancement, which were created as part of the IARPA sponsored UG$^2$Challenge workshop held at CVPR 2018. Under the proposed evaluation regime, we present an in-depth analysis of these algorithms and a host of deep learning-based and classic baseline approaches. From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.

Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation [1], and show that the network's treatment of exposure is markedly different from a real camera. We further show that this cue can be used to distinguish GAN-generated imagery from camera imagery, including effective discrimination between GAN imagery and real camera images used to train the GAN.

Image forensics is increasingly of interest, due to the deluge of online photo-sharing platforms and high-quality image editing software. While advances in computer vision enable these editing tools, they also provide a means by which to detect such tampering. Recent work in large-scale image phylogeny, for instance, aims to infer relationships between multiple images which may have contributed to a manipulation. A key task within this scope is detecting splicing between a pair of images, which we address with a combination of low-and semantic-level cues in order to provide fast detection with the false alarm rate demanded by large image collections. We show that, while deep learning approaches contribute to these ends, traditional feature-based detection still forms the basis of a useful detector, providing scale invariance without incurring the complexity associated with deep networks. Our method out-performs the state-of-the-art approach based on deep learning, measured on a challenge dataset for splice detection, and can also be used for detection of copy-move manipulations.