Offer three denoise networks and a method to choose between them

AI Denoise's Apply switch becomes Method: Bilinear, Fast, Medium, Best,
default Best, so an untouched raw writes nothing and develops through the
mixture. `apply` is still read and never written: 0 is Bilinear, 1 keeps
a network already chosen.

- Best is the mixture of a flat and an edge expert with a learned gate;
  Medium and Fast are students distilled from it. 2.48 s, 0.79 s and
  0.57 s for a 20 MP frame on TensorRT fp16.
- Each network carries its own tile border (256 for the mixture, 192 for
  the students) through `dr_denoise::Shipped` and `TileNet::halo`.
- The file is hashed once at open and each network keys its own cached
  result; Bilinear keeps the result in memory for the way back.
- Each has an .a16w16 sibling for the Hexagon: 0.00 dB on the 6D gate,
  at most 0.11 dB with the noise scaled x0.5 to x4.
- APK BUNDLED 19 -> 23; the PKGBUILD installs all three.

ui/dr-ui/src/labels.rs

@@ -223,6 +223,12 @@ fn catalogued(key: &str) -> Option<&'static str> {
         // panel under its own name — see `rows_filtered`.
         "param.lens_profile.apply" => "Apply",
         "param.learned_denoise.apply" => "Apply",
+        // Which demosaic: the classical one, or a network by how long it takes.
+        "param.learned_denoise.method" => "Method",
+        "param.learned_denoise.method.bilinear" => "Bilinear",
+        "param.learned_denoise.method.fast" => "Fast",
+        "param.learned_denoise.method.medium" => "Medium",
+        "param.learned_denoise.method.best" => "Best",
         // How strongly: 100 % is the network's result, and less puts the
         // removed noise's brightness back as grain.
         "param.learned_denoise.strength" => "Strength",