ABSTRACT In this paper, we present a novel method for inverse ltering a two dimensional (2-D) signal using phase-basedprocessing techniques. A 2-D sequence can be represented by a sucient number of samples of the phase of itsFourier transform and its region of support. This is exploited to perform deconvolution. We examine the e ectsof additive noise and incomplete knowledge of the point spread function on the performance of this deconvolutionmethod and compare it with other 2-D deconvolution methods. The problem of nding the region of supportwill also be briey addressed. Finally, an application example will be presented.Keywords: inverse ltering, deconvolution, phase-based processing 1. INTRODUCTION A common problem that arises in signal processing is that of obtaining an original sequence from a sequence thathas been ltered or blurred. This is referred to as the inverse ltering problem. We are interested speci cally ininverse ltering 2-D signals.The model we will consider is of a 2-D signal being convolved with a spatially invariant point spread function(PSF) and then corrupted by additive noise. This model is widely used in practice in elds as diverse asarcheology,
['Megan Marie Fuller', 'Jae S. Lim']
A new robust method for two-dimensional inverse filtering