In this article we introduce line Smoothness-Increasing Accuracy-Conserving Multi -Resolution Analysis. This is a procedure for exploiting convolution kernel post-processors for ob-taining more accurate multidimensional multiresolution analysis in terms of error reduction. This filtering-projection tool allows for the transition of data between different resolutions while simultane-ously decreasing errors in the fine grid approximation. It specifically allows for defining detail multi -wavelet coefficients when translating coarse data onto finer meshes. These coefficients are usually not defined in such cases. We show how to analytically evaluate the resulting convolutions and express the filtered approximation in a new basis. This is done by combining the filtering procedure with projection operators that allow for computational implementation of this scale transition procedure. Further, this procedure can be applied to piecewise constant approximations to functions, as it pro-vides error reduction. We demonstrate the effectiveness of this technique in two and three dimensions.