Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion

• Published in: IEEE transactions on image processing Vol. 16; no. 2; pp. 367 - 381
• Main Authors: Youshen Xia, Kamel, M.S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Blind Gaussian noise environments; Computer Graphics; Computer science; control theory; systems; Connectionism. Neural networks; cooperative neural fusion (CNF) algorithms; Degradation; Detection, estimation, filtering, equalization, prediction; Estimates; Exact sciences and technology; Gaussian noise; Image contrast; Image converters; Image Enhancement - methods; Image fusion; Image Interpretation, Computer-Assisted - methods; Image processing; Image restoration; Information Storage and Retrieval - methods; Information theory; Information, signal and communications theory; Neural Networks (Computer); Noise; Noise robustness; nonoptimal regularization parameters; Normal Distribution; Numerical Analysis, Computer-Assisted; Optimization; Parameter estimation; Pattern Recognition, Automated - methods; Performance analysis; Regularization; robust image estimation; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal restoration; Signal, noise; Subtraction Technique; Telecommunications and information theory; Working environment noise; Performance evaluation; Image processing; Blind Gaussian noise environments; Non gaussian noise; Robust estimation; Image restoration; Neural network; Background noise; Algorithm; Optimization; Information loss; Image quality; nonoptimal regularization parameters; cooperative neural fusion (CNF) algorithms; Gaussian noise; Simulation; Information processing; Data fusion; Image fusion; robust image estimation
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.888340

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods