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At the time of implanting bone-related implants into human body, a variety of biological responses to the material surface occur with respect to surface chemistry and physical state. The commonly used biomaterials (e.g. titanium and its alloy, Co–Cr alloy, stainless steel, polyetheretherketone, ultra-high molecular weight polyethylene and various calcium phosphates) have many drawbacks such as lack of biocompatibility and improper mechanical properties. As surface modification is very promising technology to overcome such problems, a variety of surface modification techniques have been being investigated. This review paper covers recent advances in surface modification techniques of bone-related materials including physicochemical coating, radiation grafting, plasma surface engineering, ion beam processing and surface patterning techniques. The contents are organized with different types of techniques to applicable materials, and typical examples are also described.

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones. * A. Ignatov and R. Timofte ({andrey,radu.timofte}@vision.ee.ethz.ch, ETH Zurich) are the challenge organizers, while the other authors participated in the challenge. The Appendix contains the authors' teams and affiliations. PIRM 2018 Challenge webpage: http://ai-benchmark.org