Super-resolution reconstruction of thermal infrared images

In this paper a high-resolution (HR) thermal infrared image is reconstructed from a sequence of subpixel shifted, aliased low-resolution (LR) frames, by means of a stochastic regularized super-resolution (SR) method. The Huber (H) cost function is employed to measure the difference between the projected estimate of the HR image and each LR frame. The bilateral Total Variation (TV) regularization is incorporated as a priori knowledge about the solution. The proposed HTV super-resolution approach that employs the Huber norm in combination with the bilateral TV regularization exhibits superior performance to former SR method. Thus, the effect of outliers is significantly reduced and the high-frequency edge structures of the reconstructed HR thermal infrared image are preserved. The proposed technique is also tested on frames that are corrupted by Gaussian noise and proves superior when compared to existing regularized SR method. Key-Words: super-resolution, robust estimation, Huber norm, bilateral TV, thermal infrared imaging

[1]  Vassilis Tsagaris,et al.  Interpolation in multispectral data using neural networks , 2004, SPIE Remote Sensing.

[2]  S. Jitapunkul,et al.  A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization , 2006, 2006 International Symposium on Intelligent Signal Processing and Communications.

[3]  Somchai Jitapunkul,et al.  A Robust Iterative Super-Resolution Reconstruction of Image Sequences using a Tukey's Biweigth Bayesian Approach with Fast Affine Block-Based Registration , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[4]  R. Lee Culver,et al.  Wavelength diversity based infrared super-resolution and condition-based maintenance , 2008 .

[5]  Ronald G. Driggers,et al.  NVThermIP modeling of super-resolution algorithms , 2005, SPIE Defense + Commercial Sensing.

[6]  Lin Lu,et al.  MAP algorithm to super-resolution of infrared images , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[7]  Hideyuki Tonooka.  Resolution enhancement of ASTER shortwave and thermal infrared bands based on spectral similarity , 2005, SPIE Asia-Pacific Remote Sensing.

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

[9]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[10]  Stephen J. Roberts,et al.  A Sampled Texture Prior for Image Super-Resolution , 2003, NIPS.

[11]  F. Mosteller,et al.  Understanding robust and exploratory data analysis , 1985 .

[12]  V. Anastassopoulos,et al.  Super-resolution image reconstruction employing Kriging interpolation technique , 2007, 2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services.

[13]  S. Susan Young,et al.  Applications of super-resolution and deblurring to practical sensors , 2008, SPIE Defense + Commercial Sensing.

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

[15]  Vassilis Anastassopoulos,et al.  Scanned images resolution improvement using neural networks , 2007, Neural Computing and Applications.