var desc = new IntPtr();
                int precision = DFTI.DOUBLE;
                int forward_domain = DFTI.REAL; // DFTI.COMPLEX;
                int dimension = 2;
                int[] len = { rows, columns };

                // Create the new DTFI descriptor. Got an unhandled exception here once (16-Sep-2013).
                try
                {
                    int ret = DFTI.DftiCreateDescriptor(ref desc, precision, forward_domain, dimension, len);
                }
                catch (Exception)
                {
                    // If the reset noise filter fails, just log it and ignore it. Better to have one frame without correction
                    // than to abort data collection.
                    string msg = string.Format("ResetNoise exception: {0} {1} {2} {3} {4}", precision, forward_domain, dimension, len[0], len[1]);
                    XLogger.WriteLog(XLogger.Level.Standard, TraceEventType.Warning, msg, null);
                    return frameImageData;  // throw;
                }

                // Setup the scale factor
                long transform_size = rows * columns;
                double scale_factor = 1.0 / transform_size;
                DFTI.DftiSetValue(desc, DFTI.BACKWARD_SCALE, scale_factor);

                // Setup the transform parameters
                DFTI.DftiSetValue(desc, DFTI.PLACEMENT, DFTI.NOT_INPLACE);
                DFTI.DftiSetValue(desc, DFTI.PACKED_FORMAT, DFTI.PACK_FORMAT);

                // Commit the descriptor
                DFTI.DftiCommitDescriptor(desc);

                // The data to be transformed
                var x_normal = new double[rows * columns];
                var x_transformed = new double[rows * columns];

                // Initialize the data array
                for (int y = 0; y < rows; y++)     // actually v
                    for (int x = 0; x < columns; x++) // actually u
                        x_normal[y * columns + x] = ((frameImageData[y, x] > 100.0) ? 100.0 : frameImageData[y, x]) + baseline;

                // Forward transform
                DFTI.DftiComputeForward(desc, x_normal, x_transformed);

                // Gaussian filter
                int h = len[0];
                int w = len[1];
                for (int y = 0; y < rows; y++)     // actually v
                    for (int x = 0; x < columns; x++) // actually u
                    {
                        double value = gaussianFilter(y, x, h, w);
                        x_transformed[y * columns + x] *= value;
                        //x_transformed_real[y*rows+x] *= value;
                        //x_transformed_imaginary[y * rows + x] *= value;
                    }

                // Backward transform
                DFTI.DftiComputeBackward(desc, x_transformed, x_normal);

                DFTI.DftiFreeDescriptor(ref desc);