32,450 research outputs found
Super-d-complexity of finite words
In this paper we introduce and study a new complexity measure for finite
words. For positive integer special scattered subwords, called
super--subwords, in which the gaps are of length at least , are
defined. We give methods to compute super--complexity (the total number of
different super--subwords) in the case of rainbow words (with pairwise
different letters) by recursive algorithms, by mahematical formulas and by
graph algorithms. In the case of general words, with letters from a given
alphabet without any restriction, the problem of the maximum value of the
super--complexity of all words of length is presented
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed
problem, which aims to obtain a high-resolution (HR) output from one of its
low-resolution (LR) versions. To solve the SISR problem, recently powerful deep
learning algorithms have been employed and achieved the state-of-the-art
performance. In this survey, we review representative deep learning-based SISR
methods, and group them into two categories according to their major
contributions to two essential aspects of SISR: the exploration of efficient
neural network architectures for SISR, and the development of effective
optimization objectives for deep SISR learning. For each category, a baseline
is firstly established and several critical limitations of the baseline are
summarized. Then representative works on overcoming these limitations are
presented based on their original contents as well as our critical
understandings and analyses, and relevant comparisons are conducted from a
variety of perspectives. Finally we conclude this review with some vital
current challenges and future trends in SISR leveraging deep learning
algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM
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