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Use Kernel normalization for more accurate Lanczos resampling. Lanczos2 filter added

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This commit is contained in:
jst 2012-09-04 18:49:04 +02:00
parent 339b8fd43a
commit d93161631c
2 changed files with 54 additions and 12 deletions
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8
README.md
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@ -33,6 +33,7 @@ The provided interpolation functions are
- `NearestNeighbor`: [Nearest-neighbor interpolation](http://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
- `Bilinear`: [Bilinear interpolation](http://en.wikipedia.org/wiki/Bilinear_interpolation)
- `Bicubic`: [Bicubic interpolation](http://en.wikipedia.org/wiki/Bicubic_interpolation)
- `Lanczos2`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=2
- `Lanczos3`: [Lanczos resampling](http://en.wikipedia.org/wiki/Lanczos_resampling) with a=3
Sample usage:
@ -43,6 +44,7 @@ package main
import (
"github.com/nfnt/resize"
"image/jpeg"
"log"
"os"
)
@ -50,13 +52,13 @@ func main() {
// open "test.jpg"
file, err := os.Open("test.jpg")
if err != nil {
return
log.Fatal(err)
}
// decode jpeg into image.Image
img, err := jpeg.Decode(file)
if err != nil {
return
log.Fatal(err)
}
file.Close()
@ -66,7 +68,7 @@ func main() {
out, err := os.Create("test_resized.jpg")
if err != nil {
return
log.Fatal(err)
}
defer out.Close()

58
filters.go
View File

@ -97,19 +97,59 @@ func Bicubic(x, y float32, img image.Image) color.RGBA64 {
return color.RGBA64{c[0], c[1], c[2], c[3]}
}
// 1-d convolution with windowed sinc for a=3.
func lanczos_x(x float32, p *[6]RGBA) (c RGBA) {
// 1-d convolution with windowed sinc for a=2.
func lanczos2_x(x float32, p *[4]RGBA) (c RGBA) {
x -= float32(math.Floor(float64(x)))
var v float32
var kernel float32
var sum float32 = 0 // for kernel normalization
l := [4]float32{0.0, 0.0, 0.0, 0.0}
for j := range p {
v = float32(Sinc(float64(x-float32(j-2)))) * float32(Sinc(float64((x-float32(j-2))/3.0)))
kernel = float32(Sinc(float64(x-float32(j-1)))) * float32(Sinc(float64((x-float32(j-1))/2.0)))
sum += kernel
for i := range c {
l[i] += float32(p[j][i]) * v
l[i] += float32(p[j][i]) * kernel
}
}
for i := range c {
c[i] = clampToUint16(l[i])
c[i] = clampToUint16(l[i] / sum)
}
return
}
// Lanczos interpolation (a=2).
func Lanczos2(x, y float32, img image.Image) color.RGBA64 {
xf, yf := int(math.Floor(float64(x))), int(math.Floor(float64(y)))
var row [4]RGBA
var col [4]RGBA
for i := range row {
row = [4]RGBA{toRGBA(img.At(xf-1, yf+i-1)), toRGBA(img.At(xf, yf+i-1)), toRGBA(img.At(xf+1, yf+i-1)), toRGBA(img.At(xf+2, yf+i-1))}
col[i] = lanczos2_x(x, &row)
}
c := lanczos2_x(y, &col)
return color.RGBA64{c[0], c[1], c[2], c[3]}
}
// 1-d convolution with windowed sinc for a=3.
func lanczos3_x(x float32, p *[6]RGBA) (c RGBA) {
x -= float32(math.Floor(float64(x)))
var kernel float32
var sum float32 = 0 // for kernel normalization
l := [4]float32{0.0, 0.0, 0.0, 0.0}
for j := range p {
kernel = float32(Sinc(float64(x-float32(j-2)))) * float32(Sinc(float64((x-float32(j-2))/3.0)))
sum += kernel
for i := range c {
l[i] += float32(p[j][i]) * kernel
}
}
for i := range c {
c[i] = clampToUint16(l[i] / sum)
}
return
}
@ -120,11 +160,11 @@ func Lanczos3(x, y float32, img image.Image) color.RGBA64 {
var row [6]RGBA
var col [6]RGBA
for i := 0; i < 6; i++ {
for i := range row {
row = [6]RGBA{toRGBA(img.At(xf-2, yf+i-2)), toRGBA(img.At(xf-1, yf+i-2)), toRGBA(img.At(xf, yf+i-2)), toRGBA(img.At(xf+1, yf+i-2)), toRGBA(img.At(xf+2, yf+i-2)), toRGBA(img.At(xf+3, yf+i-2))}
col[i] = lanczos_x(x, &row)
col[i] = lanczos3_x(x, &row)
}
c := lanczos_x(y, &col)
c := lanczos3_x(y, &col)
return color.RGBA64{c[0], c[1], c[2], c[3]}
}