54,258 research outputs found
MaxSR: Image Super-Resolution Using Improved MaxViT
While transformer models have been demonstrated to be effective for natural
language processing tasks and high-level vision tasks, only a few attempts have
been made to use powerful transformer models for single image super-resolution.
Because transformer models have powerful representation capacity and the
in-built self-attention mechanisms in transformer models help to leverage
self-similarity prior in input low-resolution image to improve performance for
single image super-resolution, we present a single image super-resolution model
based on recent hybrid vision transformer of MaxViT, named as MaxSR. MaxSR
consists of four parts, a shallow feature extraction block, multiple cascaded
adaptive MaxViT blocks to extract deep hierarchical features and model global
self-similarity from low-level features efficiently, a hierarchical feature
fusion block, and finally a reconstruction block. The key component of MaxSR,
i.e., adaptive MaxViT block, is based on MaxViT block which mixes MBConv with
squeeze-and-excitation, block attention and grid attention. In order to achieve
better global modelling of self-similarity in input low-resolution image, we
improve block attention and grid attention in MaxViT block to adaptive block
attention and adaptive grid attention which do self-attention inside each
window across all grids and each grid across all windows respectively in the
most efficient way. We instantiate proposed model for classical single image
super-resolution (MaxSR) and lightweight single image super-resolution
(MaxSR-light). Experiments show that our MaxSR and MaxSR-light establish new
state-of-the-art performance efficiently
Single image example-based super-resolution using cross-scale patch matching and Markov random field modelling
Example-based super-resolution has become increasingly popular over the last few years for its ability to overcome the limitations of classical multi-frame approach. In this paper we present a new example-based method that uses the input low-resolution image itself as a search space for high-resolution patches by exploiting self-similarity across different resolution scales. Found examples are combined in a high-resolution image by the means of Markov Random Field modelling that forces their global agreement. Additionally, we apply back-projection and steering kernel regression as post-processing techniques. In this way, we are able to produce sharp and artefact-free results that are comparable or better than standard interpolation and state-of-the-art super-resolution techniques
Single Frame Image super Resolution using Learned Directionlets
In this paper, a new directionally adaptive, learning based, single image
super resolution method using multiple direction wavelet transform, called
Directionlets is presented. This method uses directionlets to effectively
capture directional features and to extract edge information along different
directions of a set of available high resolution images .This information is
used as the training set for super resolving a low resolution input image and
the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform
recovers the super-resolved high resolution image. The simulation results
showed that the proposed approach outperforms standard interpolation techniques
like Cubic spline interpolation as well as standard Wavelet-based learning,
both visually and in terms of the mean squared error (mse) values. This method
gives good result with aliased images also.Comment: 14 pages,6 figure
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