Image enhancement by nonlinear wavelet processing

In this paper we describe how the theory of wavelet thresholding introduced by Donoho and Johnstone can successfully be applied to two distinct problems in image processing where traditional linear ltering techniques are insuucient. The rst application is related to speckle reduction in coherent imaging systems. We show that the proposed method works well for reducing speckle in SAR images while maintaining bright reeections for subsequent processing and detection. Secondly we apply the wavelet based method for reducing blocking artifacts associated with most DCT based image coders (e.g., most notably the Joint Photographic Experts Group (JPEG) standard at high compression ratios). In particular we demonstrate an algorithm for post-processing decoded images without the need for a novel coder/decoder. By applying this algorithm we are able to obtain perceptually superior images at high compression ratios using the JPEG coding standard. For both applications we have developed methods for estimating the required threshold parameter and we have applied these to large number of images to study the eeect of the wavelet thresholding. Our main goal with this paper is to illustrate how the recent theory of wavelet denoising can be applied to a wide range of practical problems which does not necessarily satisfy all the assumptions of the developed theory.

[1]  Ramesh A. Gopinath,et al.  Enhancement of decompressed images at low bit rates , 1994, Optics & Photonics.

[2]  Allen Gersho,et al.  Enhancement of transform coding by nonlinear interpolation , 1991, Other Conferences.

[3]  Peter N. Heller,et al.  The Spectral Theory of Multiresolution Operators and Applications , 1994 .

[4]  Thomas W. Parks,et al.  A two-dimensional translation invariant wavelet representation and its applications , 1994, Proceedings of 1st International Conference on Image Processing.

[5]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[6]  J. Goodman Some fundamental properties of speckle , 1976 .

[7]  Deepen Sinha,et al.  On the optimal choice of a wavelet for signal representation , 1992, IEEE Trans. Inf. Theory.

[8]  Ramesh A. Gopinath,et al.  Wavelet-based post-processing of low bit rate transform coded images , 1994, Proceedings of 1st International Conference on Image Processing.

[9]  G. Beylkin On the representation of operators in bases of compactly supported wavelets , 1992 .

[10]  Naoki Saito,et al.  Simultaneous noise suppression and signal compression using a library of orthonormal bases and the minimum-description-length criterion , 1994, Defense, Security, and Sensing.

[11]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[12]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[13]  Gilbert Strang,et al.  Short wavelets and matrix dilation equations , 1995, IEEE Trans. Signal Process..

[14]  Patrick Wambacq,et al.  Comparison Of Some Speckle Reduction Techniques For Sar Images , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[15]  Peter N. Heller,et al.  Regular M-band wavelets and applications , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  Ramesh A. Gopinath,et al.  Wavelet based speckle reduction with application to SAR based ATD/R , 1994, Proceedings of 1st International Conference on Image Processing.

[17]  Allen Gersho,et al.  Improved decoder for transform coding with application to the JPEG baseline system , 1992, IEEE Trans. Commun..

[18]  I. Johnstone,et al.  Minimax Risk over l p-Balls for l q-error , 1994 .

[19]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[20]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[21]  Peter N. Heller,et al.  Theory of regular M-band wavelet bases , 1993, IEEE Trans. Signal Process..

[22]  Guy P. Nason,et al.  Wavelet regression by cross-validation, , 1994 .

[23]  D. Donoho Unconditional Bases Are Optimal Bases for Data Compression and for Statistical Estimation , 1993 .

[24]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[25]  C. Burrus,et al.  Optimal wavelet representation of signals and the wavelet sampling theorem , 1994 .

[26]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[27]  C. Sidney Burrus,et al.  Wavelet-based SAR speckle reduction and image compression , 1995, Defense, Security, and Sensing.

[28]  C. Chui,et al.  On compactly supported spline wavelets and a duality principle , 1992 .

[29]  R. Coifman,et al.  Fast wavelet transforms and numerical algorithms I , 1991 .

[30]  C. Sidney Burrus,et al.  Nonlinear Processing of a Shift Invariant DWT for Noise Reduction , 1995 .

[31]  G. T. Warhola,et al.  DE-NOISING USING WAVELETS AND CROSS VALIDATION , 1995 .

[32]  G. J. Owirka,et al.  Optimal polarimetric processing for enhanced target detection , 1991, NTC '91 - National Telesystems Conference Proceedings.

[33]  Truong Q. Nguyen Near-perfect-reconstruction pseudo-QMF banks , 1994, IEEE Trans. Signal Process..