Correspondences are prevalent in natural videos among different frames, as well as a set of images sharing a common attribute. Dense correspondences are important for the core problem of many natural image and video reconstruction tasks: recovering texture details with high fidelity. In this paper, we will discuss recent methods in learning and utilizing such correspondences in image and video reconstruction. Specifically, we decompose the network design into several switchable components of different purposes and discuss their applications to different images and video restoration tasks such as super-resolution, denoising, and video frame interpolation. In this way, we can analyze the performance and uncover the generic and efficient network design. Benefiting from the above investigations, our proposed methods achieve state-of-the-art performance on multiple tasks with fewer parameters. Our findings could inspire the network design of multiple image and video reconstruction tasks for the future.
We present the results of our image analysis of portrait art from the Roman Empire's Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization's ancient past and increase appreciation of our diverse cultural heritage.