Two-dimensional LMS adaptive filter incorporating a local-mean estimator for image processing

Abstract

Abstruct-This paper concerns the application aspect of the two-dimensional (2-D) LMS adaptive filter in image processing. It is a recent research interest to extend some successful adaptive filtering techniques to the 2-D case. The LMS adaptive filter [l] has drawn attention because of its simplicity, which is significant in 2-D applications. On the basis of analyzing the strong points and weakness of the 2-D LMS adaptive filter in image processing, an adaptive scheme is proposed whereby a local-mean estimation substructure is incorporated to overcome the problem of the 2-D LMS adaptive filter in dealing with signals having a nonzero mean With, especially, abrupt variations (edges), which is a typical feature of practical images. The advantages of this scheme over the direct use of the 2-D LMS are considered. As a direct application, this scheme is used for adaptive cancellation of an artifact disturbance in an image. An implementation method of the local-mean estimation substructure with the edge-preserving property is introduced. With this local-mean estimator, the proposed scheme is applied for enhancement of images disturbed by Wide-band noises. All of the application examples presented in this paper illustrate some useful specialities of the 2-D LMS adaptive filter in image processing and the improved performance of the proposed scheme.

Adaptive recovery of motion blur point spread function from differently exposed images

“…If we ignore the noise in the image formation model, a 2D convolution with a PSF may be considered as a weighted sum of the neighboring pixels, where the weights are the corresponding values of the PSF [14,15]. The corresponding linear system is intractable due to the huge number of equations and, therefore, often poor invertible data matrix leads to wrong estimation of the weights.…”

Super‐resolution approach to overcome physical limitations of imaging sensors: An overview

“…However, this assumption is sensitive to abrupt changes of the image intensity where stationarity is not justified. The performance of a nonstationary noise filter in the vicinity of edges depends on how the local statistics are estimated (Jiang and Sawchuk, 1986;Lin et al, 1993;Ozkan et al, 1993;Rangayyan et al, 1998;Song and Pearlman, 1988). The fundamental principle behind the prior local statistics estimators is that the local statistics should be calculated over the neighboring pixels on the same side of the edge over the whole local window.…”

“…After analyzing the system models, the problems can be solved by math-ematical inverse procedure, which is implemented and located right after the output signal nodes as a postprocessor. With this signalprocessing-based approach, image noise can be removed with statistical modeling of the image and the noise (Aiazzi et al, 1998;Jiang and Sawchuk, 1986;Kuan et al, 1985;Lin et al, 1993;Ozkan et al, 1993;Rangayyan et al, 1998;Samy, 1995;Sari-Sarraf and Brzakovic, 1991;Song and Pearlman, 1988); and limited dynamic range can be improved through multiple images of the same scene taken with different exposure times (Bogoni et al, 1999;Debevec and Malik, 1997;Robertson et al, 1999;Yamada et al, 1994). There are signal processing techniques to obtain a high-resolution image from observed multiple low-resolution images; this is called super-resolution image reconstruction (Clark et al, 1985;Eren et al, 1997;Hardie et al, 1997;Hong et al, 1997;Kim and Su, 1993;Komatsu et al, 1993;Park et al, 2003;Patti and Altunbasak, 2001).…”

Two-Dimensional Adaptive Filtering using the Kalman Algorithm

“…The Kalman Adaptive Algorithm is one such algorithm, which has been applied extensively in the field of 1-D signal processing. This algorithm has faster convergence characteristics when compared to the other adaptive algorithms commonly used, namely the Least Mean Square (LMS) [2] and the Recursive Least Square (RLS) algorithms. Further, the LMS and RLS algorithms depend on user-defined factors [t and X respectively for their estimation processes.…”