The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicking, and stylization.

Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered devices. The key idea is to learn a set of kernels that upscales, filters, and blends patches of different scales guided by local structure analysis. This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts. Experimental results show that the presented approach produces comparable results to state-of-the-art algorithms while processing time is orders of magnitude faster.

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models.

In Wireless Local Area Networks (WLANs), channel management is important in achieving reliable data communications and satisfying QoS requirements. The key aspects of wireless channel management are monitoring the channel quality and adapting quickly to the network conditions by switching to a better channel. We propose a wireless channel monitoring system with co-located monitoring surrogates. Our system works on multi-radio Access Points (APs) where a co-located surrogate radio monitors the condition of various channels while the master radio serves the clients for data communication. Although we have designed our system for generic WLANs, we believe it will be most useful for IEEE 802.11n networks where there are a large number of channels and dynamic frequency selection is required. Our system enables intelligent, fast channel adaptation, reduces service disruption time, and consequently helps realize the performance potential of 802.11n. We present our multi-radio co-located wireless channel monitoring surrogate system and evaluate its effectiveness on our IEEE 802.11n network testbed. We also perform case studies to demonstrate the benefit our system brings compared against the existing schemes.

Multicasting is useful for various applications such as multimedia broadcasting. In current 802.11, multicast frames are sent as broadcast frames at a low transmission rate without any acknowledgement or binary exponential backoff. This naive multicasting mechanism degrades the performance of not only multicast flows but also unicast flows. In this paper, we propose a new multicasting mechanism based on the leader- based approach to improve the legacy multicast transmissions, maintaining coexistence with legacy 802.11 devices. Simulations show that our protocol achieves well-balanced performance in terms of reliability, latency, goodput, and transmission fairness in comprehensive environments.