Super-resolution algorithms based on atomic wavelet functions in real-time processing of video sequences

In the real world, there are a variety of applications of high resolution (HR) images in remote sensing, video frame freezing, medicine, robot artificial viewing, military information acquisition, etc. Because of the high cost and physical limitations of the acquisition hardware, the low-resolution (LR) images are used frequently. So, super-resolution (SR) restoration is an emerged solution permitting to form one or a set of HR images from a sequence of LR images. The proposed SR framework takes into account the spatial and spectral WT pixel information reconstructing different video and texture nature, presenting good performance in terms of objective (PSNR, MAE, NCD) criteria and visual subjective perception, employing the Wavelets based on atomic functions (WAF). Statistical simulations have demonstrated the effectiveness of the novel approach. The real time digital processing has been implemented on DSP of Texas Instruments TMS320DM642, demonstrating the effectiveness of the reconstruction of SR images in real time processing mode, and justifying this in the video sequences of different nature, pixel resolution and motion behavior.

[1]  H.R. Wu,et al.  A median based interpolation algorithm for deinterlacing , 2004, Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004..

[2]  Volodymyr I. Ponomaryov,et al.  Wavelets Based on Atomic Function Used in Detection and Classification of Masses in Mammography , 2008, MICAI.

[3]  Weisi Lin,et al.  Improved Super-Resolution Reconstruction From Video , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Victor Filippovich Kravchenko,et al.  Properties of Different Wavelet Filters used for Ultrasound and Mammography Compression , 2008 .

[5]  V. I. Pustovoit,et al.  A new class of WA-systems of Kravchenko-Rvachev functions , 2007 .

[6]  Xiaohai He,et al.  Video superresolution reconstruction based on subpixel registration and iterative back projection , 2009, J. Electronic Imaging.

[7]  Victor Filippovich Kravchenko,et al.  Super-Resolution Method Based on Wavelet Atomic Functions in Images and Video Sequences , 2009 .

[8]  Hartmut Schroeder,et al.  Intermediate image interpolation using polyphase weighted median filters , 2001, IS&T/SPIE Electronic Imaging.

[9]  Héctor M. Pérez Meana,et al.  Spectral Estimation of Digital Signals by the Orthogonal Kravchenko Wavelets {ha(t)} , 2009, CIARP.

[10]  Hsieh Hou,et al.  Cubic splines for image interpolation and digital filtering , 1978 .

[11]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[12]  Michael K. Ng,et al.  Wavelet algorithms for deblurring models , 2004, Int. J. Imaging Syst. Technol..

[13]  Joceli Mayer,et al.  A Robust and Computationally Efficient Simultaneous Super-Resolution Scheme for Image Sequences , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  S. Chaudhuri.  Super-Resolution Imaging , 2001 .

[15]  Michael Elad,et al.  Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences , 2006, EURASIP J. Adv. Signal Process..

[16]  N. K. Bose,et al.  High resolution image formation from low resolution frames using Delaunay triangulation , 2002, IEEE Trans. Image Process..

[17]  E. Maeland.  On the comparison of interpolation methods. , 1988, IEEE transactions on medical imaging.

[18]  Aggelos K. Katsaggelos,et al.  Super Resolution of Images and Video , 2006, Super Resolution of Images and Video.

[19]  P.N.T. Wells,et al.  Handbook of Image and Video Processing , 2001 .

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

[21]  Sebastián López,et al.  Analysis of fast block matching motion estimation algorithms for video super-resolution systems , 2008, IEEE Transactions on Consumer Electronics.

[22]  Raymond H. Chan,et al.  Wavelet Algorithms for High-Resolution Image Reconstruction , 2002, SIAM J. Sci. Comput..

[23]  Nicholas A. S. Hamm,et al.  Fuzzy Super Resolution Mapping Based on Markov Random Fields , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.