An HVS-Directed Neural-Network-Based Image Resolution Enhancement Scheme for Image Resizing

In this paper, a novel human visual system (HVS)-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.

[1]  Chun-Hsien Chou,et al.  A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[2]  Juliana Fernandes Camapum,et al.  Color texture invariants for natural image recognition based on human visual system , 2000, ICECS 2000. 7th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.00EX445).

[3]  Eric Walowit,et al.  An edge-restricted spatial interpolation algorithm , 1992, J. Electronic Imaging.

[4]  Bryan S. Morse,et al.  Isophote-based interpolation , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[5]  Nasir D. Memon,et al.  Recent Developments in Context-Based Predictive Techniques for Lossless Image Compression , 1997, Comput. J..

[6]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[7]  Thomas W. Parks,et al.  Adaptively quadratic (AQua) image interpolation , 2004, IEEE Transactions on Image Processing.

[8]  Yong-Hwan Lee,et al.  Decision-based order statistic filters , 1990, IEEE Trans. Acoust. Speech Signal Process..

[9]  Michael Unser,et al.  Enlargement or reduction of digital images with minimum loss of information , 1995, IEEE Trans. Image Process..

[10]  Shih-Kun Huang,et al.  Cocktail Watermarking for Digital Image Protection , 2000, IEEE Trans. Multim..

[11]  Jean-François Delaigle,et al.  Some improvements to HVS models for fingerprinting in perceptual decompressors , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  John W. Woods,et al.  Two-dimensional discrete Markovian fields , 1972, IEEE Trans. Inf. Theory.

[13]  Narendra Ahuja,et al.  POCS based adaptive image magnification , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[14]  Seong-Won Lee,et al.  Image interpolation using adaptive fast B-spline filtering , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  H. Lin,et al.  Incorporating human visual system (HVS) models into the fractal image compression , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[16]  Ping Wah Wong,et al.  Edge-directed interpolation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[17]  Annick Montanvert,et al.  Super-resolution inducing of an image , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Chin-Teng Lin,et al.  Satellite sensor image classification using cascaded architecture of neural fuzzy network , 2000, IEEE Trans. Geosci. Remote. Sens..

[19]  C. T. Lin,et al.  A neural fuzzy system for image motion estimation , 2000, Fuzzy Sets Syst..

[20]  José Carlos Príncipe,et al.  Super-resolution of images based on local correlations , 1999, IEEE Trans. Neural Networks.

[21]  Chin-Teng Lin,et al.  A neural fuzzy control system with structure and parameter learning , 1995 .

[22]  M. Longuet-Higgins.  Statistical properties of an isotropic random surface , 1957, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[23]  Dimitris Anastassiou,et al.  Subpixel edge localization and the interpolation of still images , 1995, IEEE Trans. Image Process..

[24]  Wenjun Zeng,et al.  Image-adaptive watermarking using visual models , 1998, IEEE J. Sel. Areas Commun..

[25]  Youngjoon Chee,et al.  Medical image compression using the characteristics of human visual system , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Thomas W. Parks,et al.  Adaptive, optimal-recovery image interpolation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[27]  Hsueh-Ming Hang,et al.  Spatially adaptive interpolation of digital images using fuzzy inference , 1996, Other Conferences.

[28]  Spyros G. Tzafestas,et al.  Neural fuzzy control systems with structure and parameter learning , 1996, J. Intell. Robotic Syst..

[29]  Haluk Derin,et al.  Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Giovanni Ramponi,et al.  A simple edge-sensitive image interpolation filter , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[31]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[32]  Jr. James E. Adams,et al.  Interactions between color plane interpolation and other image processing functions in electronic photography , 1995, Electronic Imaging.