July 31, 1998 Updated by Ms. Jade Y.S. Hsu
Vol.3 No.1, pp.111-124 (January 1987)

Image Restoration Using a Nonstationary Image Model

Long-Wen Chang and Jien-Hua Huang
Institute of Computer and Decision Sciences,
National Tsing Hua University,
Hsinchu, Taiwan (30043), ROC

        This thesis is concered with improving the fast nonrecursive algorithms for the Wiener restoration of degraded images. The degradation is assumed to be a known space-invariant point spread function and an additive white noise. An improvement is achieved by modelling the image with nonstationary model, which is more appropriate for real images than the conventional stationary model. The algorithm employs a nonstationary-mean stationary-autocovariance (NMSA) image model. The root-mean-squared error (rmse) yielded is lower than that of the wide-sense stationary image model. The specific structure of the underlying model enables the implementation of the filters using fast Fourier transform (FFT) computations.


  1. Machine Vision for Industry Prospects and Future Trends
  2. A Model-Constrained Rule-Based Pattern Matching System for Object Recognition
  3. A Computing Architecture of Adjustable Convolution System for Image Processing
  4. Fast Progressive Image Transmission without Table Look-Up, Computation and Error Propagation
  5. A New 2D Quadrature Mirror FIR Filters for Image Sub-Band Coding
  6. Image Restoration Using a Nonstationary Image Model
  7. Texture Synthesis in Image Processing and Computer Graphics