Towards Robust Evaluation of Super-Resolution Satellite Image Reconstruction

Super-resolution reconstruction (SRR) consists in processing an image or a bunch of images to generate a new image of higher spatial resolution. This problem has been intensively studied, but seldom is SRR applied in practice for satellite data. In this paper, we briefly review the state of the art on SRR algorithms and we argue that commonly adopted strategies for their evaluation do not reflect the operational conditions. We report our study on assessing the SRR outcome, relying on new quantitative measures. The obtained results allow us to outline the most important research pathways to improve the performance of SRR.

[1]  R. Molina,et al.  Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images , 2008 .

[2]  Rafael García,et al.  Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Touradj Ebrahimi,et al.  JPEG Privacy and Security framework for social networking and GLAM services , 2017, EURASIP J. Image Video Process..

[4]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for hyperspectral images , 2012, Signal Process..

[5]  Aurélien Ducournau,et al.  Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data , 2016, 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS).

[6]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yun Zhang.  PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS , 2002 .

[8]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[9]  Ruimin Hu,et al.  Face Super-Resolution via Multilayer Locality-Constrained Iterative Neighbor Embedding and Intermediate Dictionary Learning , 2014, IEEE Transactions on Image Processing.

[10]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[11]  James Hays,et al.  Super-resolution from internet-scale scene matching , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

[12]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[13]  M. Körner,et al.  SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS , 2016 .

[14]  Feng Li,et al.  Universal HMT based super resolution for remote sensing images , 2008, 2008 15th IEEE International Conference on Image Processing.

[15]  Luc Van Gool,et al.  A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution , 2014, ACCV.

[16]  Joel Ilao,et al.  Multiple-image super-resolution on mobile devices: an image warping approach , 2017, EURASIP J. Image Video Process..

[17]  Vassilis Anastassopoulos,et al.  Super-resolution image reconstruction techniques: Trade-offs between the data-fidelity and regularization terms , 2012, Inf. Fusion.

[18]  Kao-Shing Hwang,et al.  PSO-based Fusion Method for Video Super-Resolution , 2013, J. Signal Process. Syst..

[19]  Andrew Zisserman,et al.  Super-resolution enhancement of text image sequences , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[20]  Narendra Ahuja,et al.  Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Francisco de Borja Rodríguez Ortiz,et al.  A two-step neural-network based algorithm for fast image super-resolution , 2007, Image Vis. Comput..

[22]  Gholamreza Anbarjafari,et al.  IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition , 2011, IEEE Transactions on Image Processing.

[23]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[24]  Gustavo de Veciana,et al.  An information fidelity criterion for image quality assessment using natural scene statistics , 2005, IEEE Transactions on Image Processing.

[25]  Yifan Chen,et al.  Super-resolution microwave imaging: Time-domain tomography using highly accurate evolutionary optimization method , 2015, 2015 9th European Conference on Antennas and Propagation (EuCAP).

[26]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[27]  Yücel Altunbasak,et al.  Super-resolution reconstruction of hyperspectral images , 2005 .

[28]  Liangpei Zhang,et al.  Remote Sensing Image Subpixel Mapping Based on Adaptive Differential Evolution , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Thomas B. Moeslund,et al.  Super-resolution: a comprehensive survey , 2014, Machine Vision and Applications.

[30]  Bo Wu,et al.  Integrating Spatial Structure in Super-Resolution Mapping of Hyper-Spectral Image , 2012 .

[31]  Suliana Manley,et al.  A near-infrared fluorophore for live-cell super-resolution microscopy of cellular proteins. , 2013, Nature chemistry.

[32]  Yi Wang,et al.  Super-resolution mosaicking of UAV surveillance video , 2008, 2008 15th IEEE International Conference on Image Processing.

[33]  José Ranilla,et al.  Particle swarm optimization for hyper-parameter selection in deep neural networks , 2017, GECCO.

[34]  Jie Li,et al.  Image super-resolution: The techniques, applications, and future , 2016, Signal Process..

[35]  Barry Ahrens,et al.  Genetic algorithm optimization of superresolution parameters , 2005, GECCO '05.

[36]  Guangyi Chen,et al.  Enhancing Spatial Resolution of Hyperspectral Imagery Using Sensor's Intrinsic Keystone Distortion , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Hong Zhu,et al.  SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES , 2016 .