The profit-and-loss revision technique may improve the accuracy of approximation to raw image data undergone a cubic B-spline smoothing. Comments are made on this technique from the viewpoint of image smoothing and restoration

giving highlights on the equivalence between spline smoothing and diffusion smoothing

and between profit-and-loss revision and inverse diffusion restoration; formulating the revision operators into a series of renewal recursions together with an estimation to the order of their deviations from the raw data; and exposing the numerical instability of both simple and renewal recursion of the profit and loss revision. Finally

a discussion is further made on the feasibility of applying the profit-and-loss revision to edge detection for images in the presence of noise.