414 research outputs found
Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation
Learning-based image super-resolution aims to reconstruct high-frequency (HF)
details from the prior model trained by a set of high- and low-resolution image
patches. In this paper, HF to be estimated is considered as a combination of
two components: main high-frequency (MHF) and residual high-frequency (RHF),
and we propose a novel image super-resolution method via dual-dictionary
learning and sparse representation, which consists of the main dictionary
learning and the residual dictionary learning, to recover MHF and RHF
respectively. Extensive experimental results on test images validate that by
employing the proposed two-layer progressive scheme, more image details can be
recovered and much better results can be achieved than the state-of-the-art
algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium
of Circuits and Systems (ISCAS) 201
FRESH – FRI-based single-image super-resolution algorithm
In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels
Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
Hyperspectral imaging enables versatile applications due to its competence in
capturing abundant spatial and spectral information, which are crucial for
identifying substances. However, the devices for acquiring hyperspectral images
are expensive and complicated. Therefore, many alternative spectral imaging
methods have been proposed by directly reconstructing the hyperspectral
information from lower-cost, more available RGB images. We present a thorough
investigation of these state-of-the-art spectral reconstruction methods from
the widespread RGB images. A systematic study and comparison of more than 25
methods has revealed that most of the data-driven deep learning methods are
superior to prior-based methods in terms of reconstruction accuracy and quality
despite lower speeds. This comprehensive review can serve as a fruitful
reference source for peer researchers, thus further inspiring future
development directions in related domains
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