Previous attempts to use time subtraction intravenous digital subtraction angiography for ventricular imaging have been hampered by artifacts due to cardiac and respiratory motion. We have previously reported a motion‐immune dual‐energy technique in which kVp is switched between 60 and 120, at 300–500 mA, 30 times/s. In order to quantitate parameters such as ejection fraction and left ventricular volume, it is necessary to correct for scatter and veiling glare (SVG), which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). In this report, a convolution filtering method has been investigated to estimate SVG in DSA images. In the first step, a grey level transformation of the detected image is utilized to get an estimated SVG image. In the second step this image is convolved to produce an image with appropriate spatial frequency content. Estimates of SVG in several Humanoid chest phantom images were obtained using Gaussian convolution kernels with a full width at half‐maximum (FWHM) of 51–125 pixels. The root‐mean‐square (rms) percentage error of these estimates was obtained by comparison with direct SVG measurement. A convolution kernel with a FWHM of 75 pixels in each dimension applied to 16 Humanoid phantom images with various projections, thicknesses, and beam energies resulted in an average rms percentage error of 9.7% in the SVG estimate, for the 16 cases studied. The SVG estimation consisting of grey scale‐to‐SVG fraction lookup table (LUT) is made based on previous measurements. The x‐ray settings required for each patient are utilized to alter the LUT in order to account for patient thickness variations. The technique is easy to implement and can be performed within a few seconds, without any additional patient exposure.