By Jordi Salvador
Example-Based great Resolution offers a radical advent and evaluation of example-based tremendous answer, masking the main profitable algorithmic ways and theories at the back of them with implementation insights. It additionally describes present demanding situations and explores destiny trends.
Readers of this booklet might be capable of comprehend the newest common photograph patch statistical versions and the functionality limits of example-based large answer algorithms, opt for the easiest cutting-edge algorithmic replacement and track it for particular use circumstances, and fast positioned into perform implementations of the most recent and such a lot profitable example-based super-resolution methods.
- Provides specified assurance of concepts and implementation info which were effectively brought in assorted and critical real-world applications
- Covers a large choice of desktop studying ways, starting from cross-scale self-similarity innovations and sparse coding, to the newest advances in deep learning
- Presents a statistical interpretation of the subspace of traditional photo patches that transcends great solution and makes it a helpful resource for any researcher on photo processing or low-level vision
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Extra resources for Example-Based Super Resolution
4): 1. Determine the best cluster (linearization) for each patch 2. Apply the precomputed linear mapping function to each patch 3. Reconstruct the high-resolution image by patch overlapping The Anchored Neighbor Regression (ANR) algorithm by Timofte et al. (2013) proposed to relax the sparsity constraint in Eq. 2) and anchor the set of most similar atoms to each entry, resulting in a L2 -regularized Low-resolution dictionary L 1. R1…RN 2. a a 0 =1 Ra . Linear regression matrices Fig. 4 In the anchored regression approach, an external database composed of a low-resolution dictionary and a set of linear regression matrices to map the low-resolution examples to their high-resolution counterparts is computed ofﬂine.
As a postprocessing stage, we can apply Iterative Back-Projection (Irani & Peleg, 1991; J. , 2010) to ensure that the information of the input image is completely contained in the high-resolution estimate: X (n+1) : = X (n) + Hu ∗ ((Y − (X (n) ∗ Hd ) ↓ s) ↑ s). 6) The algorithm typically converges after two or three iterations. The upscaling (Hu ) and downscaling (Hd ) kernels are the ones corresponding bicubic resizing. 2 Performance Commonly used datasets in example-based super-resolution include Kodak, with 24 images of 768 × 512 pixels, or the 500 images in the Berkeley segmentation dataset from the project website of Arbelaez, Maire, Fowlkes, and Malik (2011), with 481 × 321 pixels each.
By doing so, we are generating pairs of low-frequency references (in Yl ) and their Example-Based Super Resolution. 00003-4 Copyright © 2017 Elsevier Ltd. All rights reserved. 31 32 Example-Based Super Resolution corresponding high-frequency examples (in Yh ). We should note that Yl has the same normalized bandwidth as Xl and, most importantly, that the crossscale self-similarity property is also present between these two images. Let xl,i be a patch of Np × Np pixels with the central pixel located at λ(xl,i ) = (ri , ci ) within Xl .