Reconstruction- and example-based super-resolution
(SR) methods are promising for restoring a high-resolution
(HR) image from low-resolution (LR) image(s). Under large
magnification, reconstruction-based methods usually fail
to hallucinate visual details while example-based methods
sometimes introduce unexpected details. Given a generic
LR image, to reconstruct a photo-realistic SR image and
to suppress artifacts in the reconstructed SR image, we
introduce a multi-scale dictionary to a novel SR method
that simultaneously integrates local and non-local priors.
The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local
area. The non-local prior enriches visual details by taking
a weighted average of a large neighborhood as an estimate
of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate
that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
New applications such as video conferencing, video on demand, multi-
media transcoders, Voice-over-IP (VoIP), intrusion detection, distributed
collaboration, and intranet security require advanced functionality from
networks beyond simple forwarding congestion control techniques.
Emerging technologies such as WiFi and WiMAX are profoundly changing the
landscape of wireless broadband. As we evolve into future generation wireless
networks, a primary challenge is the support of high data rate, integrated multi-
media type traffic over a unified platform. Due to its inherent advantages in
high-speed communication, orthogonal frequency division multiplexing (OFDM)
has become the modem of choice for a number of high profile wireless systems
(e.g., DVB-T, WiFi, WiMAX, Ultra-wideband).
Employing multiple transmit and receive antennas, namely using multi-input multi-output
(MIMO) systems, has proven to be a major breakthrough in providing reliable wireless
communication links. Since their invention in the mid-1990s, transmit diversity, achieved
through space-time coding, and spatial multiplexing schemes have been the focus of much
research in the area of wireless communications.
Today’s wireless services have come a long way since the roll out of the
conventional voice-centric cellular systems. The demand for wireless access
in voice and high rate data multi-media applications has been increasing.
New generation wireless communication systems are aimed at accommodating
this demand through better resource management and improved transmission
technologies.
Mobile communication has gained significant importance in today’s society. As
of 2010, the number of mobile phone subscribers has surpassed 5 billion [ABI10],
and the global annual mobile revenue is soon expected to top $1 trillion [Inf10].
While these numbers appear promising for mobile operators at first sight, the
major game-changer that has come up recently is the fact that the market is
more and more driven by the demand for mobile data traffic [Cis10].
The writing of this book was prompted by two main developments in wireless
communications in the past decade. First is the huge surge of research activities in
physical-layer wireless communication theory. While this has been a subject of study
since the 60’s, recent developments in the field, such as opportunistic and multi-input
multi-output (MIMO) communication techniques, have brought completely new per-
spectives on how to communicate over wireless channels.
In this book, we study the interference cancellation and detection problem in
multiantenna multi-user scenario using precoders. The goal is to utilize multiple
antennas to cancel the interference without sacrificing the diversity or the com-
plexity of the system.
