Edge detection with iteratively refined regularization

An approach to edge detection problems using regularization theory is presented. The energy functional in the standard regularization has been modified to control the smoothness over the image spatially in order to obtain accurate location of edges. An algorithm which iteratively improves the solution in discontinuous regions by updating the space-varying regularization parameter has been developed. The regularization parameter is controlled by features extracted from the error signal between the regularized solution obtained in the previous iteration and the image data. The algorithm smooths the noisy image without degrading discontinuities. It offers computational advantages and an efficient alternative to existing algorithms for edge detection and for surface reconstruction. The application of the proposed algorithm to various synthetic and real images is discussed