A Technical Review on Image Super-Resolution Techniques

Conversion from multiple low resolution (LR) images to high resolution (HR) image is done by using super-resolution techniques. Anyone can achieve more information in detail from high-resolution images, which helps further for many satellite image applications. This growing technology interest in the reconstruction of imagery leads to several methodologies in the field of advanced digital color image processing. The study presents and describes the various conventional algorithms of image SR reconstruction used to date by researchers. In this review paper, we have shown different types of super-resolution techniques starting from image noisy to remotely sensed imagery for super-resolution mapping at a sub-pixel level with their characteristics and major limitations. A precise comparative study was done on different domains like spatial and frequency as shown separately in tabular form.

[1]  Ali Abedi,et al.  Stroke width-based directional total variation regularisation for document image super resolution , 2016, IET Image Process..

[2]  Yuesheng Xu,et al.  High-resolution image reconstruction: An envℓ1 /TV model and a fixed-point proximity algorithm , 2017 .

[3]  Jun Sun,et al.  Multi-Frame Super-Resolution Reconstruction Based on Gradient Vector Flow Hybrid Field , 2017, IEEE Access.

[4]  Mudar Sarem,et al.  A novel reconstruction model for multi-frame super-resolution image based on lmix prior , 2014, Comput. Electr. Eng..

[5]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[6]  P. Atkinson,et al.  Sub-pixel mapping of remote sensing images based on radial basis function interpolation , 2014 .

[7]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[8]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[9]  Jorge Núñez,et al.  Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[11]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[12]  Andrew F. Laine,et al.  Wavelet Theory and Application , 1993, Springer US.

[13]  Tom Drummond,et al.  Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[14]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[15]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[16]  H. Tian,et al.  Spatial and temporal patterns of China's cropland during 1990¿2000: An analysis based on Landsat TM data , 2005 .

[17]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[18]  Li Peng,et al.  Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution , 2016, IEEE Transactions on Cybernetics.

[19]  Liangpei Zhang,et al.  Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Robert L. Stevenson,et al.  A Bayesian approach to image expansion for improved definitio , 1994, IEEE Trans. Image Process..

[22]  Seunghyeon Rhee,et al.  Discrete cosine transform based regularized high-resolution image reconstruction algorithm , 1999 .

[23]  A. S. Fruchter,et al.  Drizzle: A Method for the Linear Reconstruction of Undersampled Images , 1998 .

[24]  A. Murat Tekalp,et al.  High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[25]  W. Zeng,et al.  A robust variational approach to super-resolution with nonlocal TV regularisation term , 2013 .

[26]  Fei Xiao,et al.  Interpolation-based super-resolution land cover mapping , 2013 .

[27]  Fei Xiao,et al.  Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images , 2010 .

[28]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[29]  Suresh Merugu,et al.  Subpixel level mapping of remotely sensed image using colorimetry , 2017 .

[30]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[31]  Weili Zeng,et al.  Image super-resolution employing a spatial adaptive prior model , 2015, Neurocomputing.

[32]  Naima Kaabouch,et al.  A robust iterative super-resolution mosaicking algorithm using an adaptive and directional Huber-Markov regularization , 2016, J. Vis. Commun. Image Represent..

[33]  Pierre-Marc Jodoin,et al.  Novel Graph Cuts Method for Multi-Frame Super-Resolution , 2015, IEEE Signal Processing Letters.

[34]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[35]  Aggelos K. Katsaggelos,et al.  Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images , 1995, Proceedings., International Conference on Image Processing.