A Comprehensive Review of Deep Learning-based Single Image Super-resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table

Perspective Texture Synthesis Based on Improved Energy Optimization

<div><p>Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it’s time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.</p></div

Energy optimization process on sparse.

<p>Energy optimization process on sparse.</p

Perspective texture synthesis of a flower image.

<p>(a) the input example; (b) scale map with σ = 60°, τ = 20°; (c) our result; (d) optimization [Kwatra et al. 2005] result.</p

Texture scale variation using viewer centered spherical coordinate system.

<p>Tilt components of surface orientation is illustrated using a set of round patches arranged on a sphere. Central line at each patch shows the direction of the surface normal.</p

Some examples of perspective texture, which apparently have visual properties.

<p>These three texture images will be texture samples of our approach, as input, to show the synthesis effect.</p

Neighborhood number: An example of replacing pixel-based computation with 2*2 patch.

<p>Neighborhood number: An example of replacing pixel-based computation with 2*2 patch.</p