61,184 research outputs found
Single frame super-resolution image system
The estimation of some unknown quantity information from known observable information can be viewed as a specific statistical process which needs an extra source of information prediction strategy. In this regard, image super-resolution is an important application In this thesis, we proposed a new image interpolation method based on Redundant Discrete Wavelet Transform (RDWT) and self-adaptive processes in which edge direction details are considered to solve single-frame image super-resolution task. Information about sharp variations, both in horizontal and vertical directions derived from wavelet transform sub-bands are considered, followed by detection and modification of the aliasing part in the preliminary output in order to increase the visual effect. By exploiting fundamental properties of images such as property of edge direction, different parts of the source image are considered separately in order to predict the vertical and horizontal details accurately, helping to consummate the whole framework in reconstructing the high-resolution image. Extensive tests of the proposed method show that both objective quality (PSNR) and subjective quality are obviously improved compared to several other state-of-the-art methods. And this work also leaved capacious space for further research, not only theoretical but also practical. Some of the related research applications based on this algorithm strategy are also briefly introduced
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual
modulation of restoration levels is of great importance for real-world
applications, but has failed most of existing deep learning based image
restoration methods. Learning from discrete and fixed restoration levels, deep
models cannot be easily generalized to data of continuous and unseen levels.
This topic is rarely touched in literature, due to the difficulty of modulating
well-trained models with certain hyper-parameters. We make a step forward by
proposing a unified CNN framework that consists of few additional parameters
than a single-level model yet could handle arbitrary restoration levels between
a start and an end level. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available:
https://github.com/hejingwenhejingwen/AdaF
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url:
https://github.com/lightChaserX/Awesome-Hetero-reI
Wideband Waveform Design for Robust Target Detection
Future radar systems are expected to use waveforms of a high bandwidth, where
the main advantage is an improved range resolution. In this paper, a technique
to design robust wideband waveforms for a Multiple-Input-Single-Output system
is developed. The context is optimal detection of a single object with
partially unknown parameters. The waveforms are robust in the sense that, for a
single transmission, detection capability is maintained over an interval of
time-delay and time-scaling (Doppler) parameters. A solution framework is
derived, approximated, and formulated as an optimization by means of basis
expansion. In terms of probabilities of detection and false alarm, numerical
evaluation shows the efficiency of the proposed method when compared with a
Linear Frequency Modulated signal and a Gaussian pulse.Comment: This paper is submitted for peer review to IEEE letters on signal
processin
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