267,868 research outputs found
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
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1
- …