run_benchmark.py KSigma Transform

[touchwolf]

What do anchor and V mean in class KSigma?

[bigeagle]

V means value range. For a typical 10bit sensor with black_level 64, V = 1023-64 = 959. Some sensors can be set in different bitwidth output, this param is useful in this case.

anchor is an extension to the original k-sigma transfor. For a random ISO Gx and an anchor ISO Ga, the process is

img_norm = inv_ksigma(ksigma(img, Gx), Ga)

In this way, the input images are "normalized" to noise characteristics at ISO-level at Ga. Compared with the orignal k-sigma transform, this extension can keep the value range to the network in a stable range.

[dcrmg]

@

V means value range. For a typical 10bit sensor with black_level 64, V = 1023-64 = 959. Some sensors can be set in different bitwidth output, this param is useful in this case.

anchor is an extension to the original k-sigma transfor. For a random ISO Gx and an anchor ISO Ga, the process is

img_norm = inv_ksigma(ksigma(img, Gx), Ga)

In this way, the input images are "normalized" to noise characteristics at ISO-level at Ga. Compared with the orignal k-sigma transform, this extension can keep the value range to the network in a stable range.

What's the use of parameter "inp_scale"(The default value is 256), Is it to increase the range of value to a certain range such as 0~256?

[bigeagle]

inp_scale is simply the network input range. The released model is trained at [0, 256].