cls.denoising_radius = IntProperty(
name="Denoising Radius",
description="Size of the image area that's used to denoise a pixel (higher values are smoother, but might lose detail and are slower)",
- min=1, max=50,
+ min=1, max=25,
default=8,
)
cls.denoising_relative_pca = BoolProperty(
}
else {
kernel_write_pass_float3_variance(buffer + kernel_data.film.pass_denoising_data + DENOISING_PASS_COLOR,
- sample, L_sum);
+ sample, ensure_finite3(L_sum));
}
kernel_write_pass_float3_variance(buffer + kernel_data.film.pass_denoising_data + DENOISING_PASS_NORMAL,
* symmetrical positive-semidefinite by construction, so we can just use this function with A=Xt*W*X and y=Xt*W*y. */
ccl_device_inline void math_trimatrix_vec3_solve(ccl_global float *A, ccl_global float3 *y, int n, int stride)
{
- math_trimatrix_add_diagonal(A, n, 1e-4f, stride); /* Improve the numerical stability. */
+ /* Since the first entry of the design row is always 1, the upper-left element of XtWX is a good
+ * heuristic for the amount of pixels considered (with weighting), therefore the amount of correction
+ * is scaled based on it. */
+ math_trimatrix_add_diagonal(A, n, 3e-7f*A[0], stride); /* Improve the numerical stability. */
math_trimatrix_cholesky(A, n, stride); /* Replace A with L so that L*Lt = A. */
/* Use forward substitution to solve L*b = y, replacing y by b. */