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Use custom "cross" filtering.

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Sobel was giving too many false positives. This cross seems to detect
the kinds of edges we care about and avoids the rest of the image, and
it does so on pretty much everything I've tried it on. Very satisfying.
This commit is contained in:
Steven Robertson 2011-10-29 17:36:06 -04:00
parent 0936e34b88
commit b240fc8459
1 changed files with 23 additions and 26 deletions
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49
cuburn/code/filtering.py
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@ -155,29 +155,26 @@ void density_est(float4 *pixbuf, float4 *outbuf,
if (in.w > 0 && den > 0) { if (in.w > 0 && den > 0) {
// Compute a Sobel derivative, The Terrible Way! Because I'm Lazy. // Compute a fast and dirty approximation of a "gradient" using
// Or more to the point, because even this sloppy mess is such a // a [[-1 0 0][0 0 0][0 0 1]]/4 matrix (and its reflection)
// small part of the overall runtime that it's not yet worth // for angled edge detection, and limit blurring in those regions
// optimizing. Using a [3, 10, 3] Sobel, normalized to represent // to both provide a bit of smoothing and prevent irregular
// the density change over a single pixel. // bleed-out along gradients close to the image grid.
const float corner = 0.0625f; // (3/16)/3 //
const float main = 0.208333333f; // (10/16)/3 // For such a simple operator - particularly one whose entire
// justification is "it feels right" - it gives very good results
// over a wide range of images without any per-flame
// parameter tuning. In rare cases, color clamping and extreme
// palette changes can cause aliasing to reappear after the DE
// step; the only way to fix that is through a color-buffer AA
// like MLAA.
float *dens = reinterpret_cast<float*>(pixbuf); float *dens = reinterpret_cast<float*>(pixbuf);
int didx = idx * 4 + 3; int didx = idx * 4 + 3;
float v = 0.0f, h = 0.0f, p; float x = 0.25f * ( dens[didx+{{info.acc_stride*4}}+4]
p = corner * dens[didx-{{info.acc_stride*4}}-4]; - dens[didx-{{info.acc_stride*4}}-4] );
v -= p; h -= p; float y = 0.25f * ( dens[didx+{{info.acc_stride*4}}-4]
v -= main * dens[didx-{{info.acc_stride*4}}]; - dens[didx-{{info.acc_stride*4}}+4] );
p = corner * dens[didx-{{info.acc_stride*4}}+4]; float diag_mag = sqrtf(x*x + y*y);
v -= p; h += p;
h -= main * dens[didx-4];
h += main * dens[didx+4];
p = corner * dens[didx+{{info.acc_stride*4}}-4];
v += p; h -= p;
v += main * dens[didx+{{info.acc_stride*4}}];
p = corner * dens[didx+{{info.acc_stride*4}}-4];
v += p; h += p;
float sobel_mag = sqrtf(v*v + h*h);
float ls = k1 * logf(1.0f + in.w * k2) / in.w; float ls = k1 * logf(1.0f + in.w * k2) / in.w;
in.x *= ls; in.x *= ls;
@ -192,12 +189,12 @@ void density_est(float4 *pixbuf, float4 *outbuf,
// then scaled in inverse proportion to the density of the point // then scaled in inverse proportion to the density of the point
// being scaled. // being scaled.
float sd = est_sd * powf(den+1.0f, neg_est_curve); float sd = est_sd * powf(den+1.0f, neg_est_curve);
// And for the Sobel... // And for the gradient...
float sobel_sd = est_sd * powf(sobel_mag+1.0f, neg_est_curve); float diag_sd = est_sd * powf(diag_mag+1.0f, neg_est_curve);
// If the Sobel SD is smaller than the minimum SD, we're probably // If the gradient SD is smaller than the minimum SD, we're probably
// on a strong edge; blur with a standard deviation of 1px. // on a strong edge; blur with a standard deviation around 1px.
if (sobel_sd < fmaxf(est_min, MIN_SD)) sd = fminf(sd, 0.5f); if (diag_sd < fmaxf(est_min, MIN_SD)) sd = 0.333333f;
// Clamp the final standard deviation. // Clamp the final standard deviation.
sd = fminf(MAX_SD, fmaxf(sd, est_min)); sd = fminf(MAX_SD, fmaxf(sd, est_min));