BM3D-r7 | state-of-the-art image/video denoising filter

[mawen1250]

Source & Readme (https://github.com/HomeOfVapourSynthEvolution/VapourSynth-BM3D)

Binary: GitHub (https://github.com/HomeOfVapourSynthEvolution/VapourSynth-BM3D/releases) | NMM (https://www.nmm-hd.org/upload/get~y1StLOycTvs/BM3D-r7.7z)

After over a month's effort, it's finally completed.
Including the 2D(image) denoising filter BM3D and the 3D(video) denoising filter V-BM3D.

The computational complexity of this denoising algorithm is fairly high, thus it's very slow.
Moreover, the memory consumption of V-BM3D is very high. Since for each current frame, multiple frames are requested, and the filtering result is also aggregated to those frames.

For convenience, I wrote a script named mvsfunc (http://forum.doom9.org/showthread.php?p=1737309), with which you could apply it with a simple call like:
import mvsfunc as mvf

core.max_cache_size = 4000 # Set a big enough cache size. For V-BM3D, you may need 8000 or even more (according to resolution and radius)

# Any input format
clip = mvf.BM3D(clip, sigma=[3,3,3], radius1=0) # radius1=0 for BM3D, radius1>0 for V-BM3D
# Same as input format

[mawen1250]

Update r2

Fix memory leaks in VAggregate.

[mawen1250]

Update r3

Fixed a stupid mistake that the order of applying DCT and IDCT is reversed (it should be DCT->filter->IDCT), which leads to over-filtering in fine structures and produces artifacts such as blocking, ringing and desaturation.
Now it actually filters as expected.

[MonoS]

I get incorrect result disabling chroma processing in OPP colorspace [also disabling only one channel]

res = edi.nnedi3_resample(crop, crop.width, crop.height, mats="601", fulls=False, curves="709", sigmoid=True, scale_thr=1.0, csp=vs.RGB48)

res = core.bm3d.RGB2OPP(res)
ref = core.bm3d.VBasic(res, radius=1, matrix=100, sigma=[8,0,0]).bm3d.VAggregate(radius=1)
flt = core.bm3d.VFinal(res, ref, radius=1, matrix=100, sigma=[8,0,0]).bm3d.VAggregate(radius=1)
flt = core.bm3d.OPP2RGB(flt)

flt = edi.nnedi3_resample(flt, flt.width, flt.height, matd="601", fulld=False, curves="709", sigmoid=True,scale_thr=1.0, csp = vs.YUV420P16)


IIRC OPP is similar to YUV so this operation should be possible, correct me if i'm wrong

[mawen1250]

Ah, I forgot to mention it in the README...
When specific plane is not processed for V-BM3D, the result of that plane will be garbage...Thus you should manually use std.ShufflePlanes to merge them.
It's mainly becanse the implementaion of V-BM3D is divided into 2 functions, it's not very convenient and efficient to pass through the unprocessed planes.

Later I will add the notification and example about it to the README.

[mawen1250]

Update r4

Added SSE2 optimizations
Basic, Final: profile="fast" use group_size=8 instead of 16 for better speed
OPP2RGB, RGB2OPP: now override frame property "_Matrix"
Compiled with VS2015

~35% faster with the same settings
~175% faster with default settings for bm3d.Basic+bm3d.Final

[MonoS]

This goes in my backlog of avx optimization :D

[mawen1250]

Update r5

Final estimate: Fixed NaN or INF results which may produce corrupt output and/or break subsequent filters

[Hagi]

Thanks mawen1250. It is, by far, the very best denoising filter I've ever used.

In case you use Paypal, I'd be happy to pay you a few beers. Cheers!

[mawen1250]

Thanks mawen1250. It is, by far, the very best denoising filter I've ever used.

In case you use Paypal, I'd be happy to pay you a few beers. Cheers!
Wow, thanks for your support and I'm glad you like this filter, that's the best reward for me. I'll PM you my Paypal.

[Hagi]

Wow, thanks for your support and I'm glad you like this filter, that's the best reward for me. I'll PM you my Paypal.

You're welcome. Hard work deserves a fair reward, within your means.

[asarian]

Looks wonderful! :) What is the best way to pre-calculate core.max_cache_size, though? Is there some kind of formula I can use?

[mawen1250]

Looks wonderful! :) What is the best way to pre-calculate core.max_cache_size, though? Is there some kind of formula I can use?
Just did a simple test with mvf.BM3D.
1920x1080 input
threads=8, max_cache_size=20000
profile="fast"

radius | Memory consumption
0 | ~4GB
1 | ~10GB
2 | ~15GB

Then try lowering max_cache_size to see if there will be obvious speed penalty. From my tests, 2/3 the memory consumption above is a well-balanced point.
That is, 2500 for radius=0, 7000 for radius=1, 10000 for radius=2, for 1920x1080 video.

[asarian]

Thank you very much! :)

I only have 12G installed, so I guess I can forget about 'radius=5' then. (Although that was on a 640x480 source, so maybe I can make it work anyway)

[jackoneill]

Just did a simple test with mvf.BM3D.
1920x1080 input
threads=8, max_cache_size=20000
profile="fast"

radius | Memory consumption
0 | ~4GB
1 | ~10GB
2 | ~15GB

Then try lowering max_cache_size to see if there will be obvious speed penalty. From my tests, 2/3 the memory consumption above is a well-balanced point.
That is, 2500 for radius=0, 7000 for radius=1, 10000 for radius=2, for 1920x1080 video.

How many frames did you filter? The cache reevaluates its behaviour every few hundred frames, so the speed/memory consumption may improve (or degrade) over time.

[mawen1250]

How many frames did you filter? The cache reevaluates its behaviour every few hundred frames, so the speed/memory consumption may improve (or degrade) over time.
approximate 500 frames

From previous experience, the memory consumption of VS will gradually decrease during encoding procedure. It's an interesting question if it will decrease when V-BM3D is used, and we can set lower max_cache_size? Maybe I'll do more tests to find it out.

[asarian]

How many frames did you filter? The cache reevaluates its behaviour every few hundred frames, so the speed/memory consumption may improve (or degrade) over time.

I noticed that, the other day. The other day I set

core.max_cache_size = 4096

(Using TempLinearApproximateMC), just to see what would happen. Python started off at ca 6G (as expected for the job), but slowly tapered off to almost not using the cache at all. Seems Python is clever enough to realize what it actually needs, over time. I like that. :) (And kinda logical too: it is, after all, max_cache_size, not just cache_size).

[mawen1250]

I noticed that, the other day. The other day I set

core.max_cache_size = 4096

(Using TempLinearApproximateMC), just to see what would happen. Python started off at ca 6G (as expected for the job), but slowly tapered off to almost not using the cache at all. Seems Python is clever enough to realize what it actually needs, over time. I like that. :) (And kinda logical too: it is, after all, max_cache_size, not just cache_size).
Python is just an interface for vs though. it's the vs core that manages the frame cache (which is written in C++).

By the way, I've tested processing more frames with BM3D(radius=2).
Up to now, the result is:
frame memory(MB)
100 15500
750 15500
1950 15300
8400 13400
37600 12700

[asarian]

Is this, despite its wicked memory requirements, perchance a good alternative to KNLMeansCL? (Something I could use on my VM, sans relevant GPU, to tide me over until I have the GTX 750 for it).

[feisty2]

Is this, despite its wicked memory requirements, perchance a good alternative to KNLMeansCL? (Something I could use on my VM, sans relevant GPU, to tide me over until I have the GTX 750 for it).

NLMeans -> for white noise
BM3D -> for Gaussian noise

Gaussian is just one kind of white noise, other kinds are out there also.

different algorithms, these two work best when combined together, not one replacing another, you could over de-noise the image a tiny little bit with BM3D and then refine it with an NLMeans loop, the result will be stunning and better than both plain BM3D or plain NLMeans

[asarian]

Okay, thanks for the explanation.

[mawen1250]

It's the V-BM3D that requires lots of memory, for spatial BM3D it's mostly acceptable.
Original NLMeans and BM3D were both designed for Gaussian white noise. In the paper, their models are built upon Gaussian white noise assumption, and their performance is also measured with Gaussian white noise.
From my experience, KNLMeans works great with anime-style sources, it'll smooth flat area and won't filter edges much.
On the contrary, BM3D will filter edges more, resulting in a more uniform de-noising. it's more suitable for non-anime content, and it's also really good to eliminate blocking and ringing in highly-compressed sources.

[feisty2]

KNLMeans works great with anime-style sources, it'll smooth flat area and won't filter edges much.

depends, generally larger s = smoother edge but more ringings...
s=4 leaves lots of ringing like residual noise around edges on VERY grainy clips
s=0/1 slays at ringing removal, it vaporizes ringings with little costs to the edge sharpness

[mawen1250]

With s=0, NLMeans falls back to something like Bilateral filter with box spatial weighting. Narrowly speaking it's not NLMeans any more (whose weighting is based on neighborhood similarity).

[feisty2]

With s=0, NLMeans falls back to something like Bilateral filter with box spatial weighting. Narrowly speaking it's not NLMeans any more (whose weighting is based on neighborhood similarity).

right, and actually s=0 and 1 are for different kinds of ringings..
s=0 (like an alternative kind of blurring, weighting on error instead of spatial distance) is good for overshoot kind of stuff
s=1, the real NLMeans is good for mosquito noise

[asarian]

different algorithms, these two work best when combined together, not one replacing another, you could over de-noise the image a tiny little bit with BM3D and then refine it with an NLMeans loop, the result will be stunning and better than both plain BM3D or plain NLMeans

So, I'd do a BM3D (radius1=0), for a Gaussian noise reduction (per frame) first, and then follow up with a KNLmeansCL pass, right?

[mawen1250]

So, I'd do a BM3D (radius1=0), for a Gaussian noise reduction (per frame) first, and then follow up with a KNLmeansCL pass, right?

If you want a very clean output, it's right. KNLMeans after BM3D will give a noise-free result with smooth flat area.
Though I think feisty2 was referring to combining them in frequency domain or to using one as the reference for another, which is mainly for less detail loss.

[feisty2]

exactly, I was talking about refining with NLMeans, not simply appending an NLMeans after BM3D

[Elegant]

Would you be better off with BM3D refined with NLMeans over just NLMeans for anime content? Or would their still be too much filtering of the edges?

[asarian]

exactly, I was talking about refining with NLMeans, not simply appending an NLMeans after BM3D

I'm sure you were. :) Can you give a crude example, though, please, of how you would 'nest' the two like that, though?

[feisty2]

I'm sure you were. :) Can you give a crude example, though, please, of how you would 'nest' the two like that, though?


import vapoursynth as vs
import mvmulti
core = vs.get_core()

fmtc_args = dict (fulls=True, fulld=True)
msuper_args = dict (chroma=False, hpad=32, vpad=32, sharp=2, levels=0)
manalyze_args = dict (search=3, chroma=False, truemotion=False, trymany=True, levels=0, badrange=-24)
mrecalculate_args = dict (chroma=False, truemotion=False, search=3, smooth=1)

def freq_merge (low, hi, p=8):
core = vs.get_core ()
Resample = core.fmtc.resample
MakeDiff = core.std.MakeDiff
MergeDiff = core.std.MergeDiff
def gauss (src):
upsmp = Resample (src, src.width * 2, src.height * 2, kernel="gauss", a1=100, **fmtc_args)
clip = Resample (upsmp, src.width, src.height, kernel="gauss", a1=p, **fmtc_args)
return clip
hif = MakeDiff (hi, gauss (hi))
clip = MergeDiff (gauss (low), hif)
return clip

def padding (src, left=0, right=0, top=0, bottom=0):
core = vs.get_core ()
Resample = core.fmtc.resample
w = src.width
h = src.height
clip = Resample (src, w+left+right, h+top+bottom, -left, -top, w+left+right, h+top+bottom, kernel="point", **fmtc_args)
return clip

def getvectors (src, pelclip=None, tr=6, pel=1, thsad=400):
core = vs.get_core ()
MSuper = core.mvsf.Super
MAnalyze = mvmulti.Analyze
MRecalculate = mvmulti.Recalculate
supersoft = MSuper (src, pelclip=pelclip, rfilter=4, pel=pel, **msuper_args)
supersharp = MSuper (src, pelclip=pelclip, rfilter=2, pel=pel, **msuper_args)
vmulti = MAnalyze (supersoft, overlap=16, blksize=32, tr=tr, **manalyze_args)
vmulti = MRecalculate (supersoft, vmulti, overlap=8, blksize=16, tr=tr, thsad=thsad/2, **mrecalculate_args)
vmulti = MRecalculate (supersharp, vmulti, overlap=4, blksize=8, tr=tr, thsad=thsad/2, **mrecalculate_args)
vmulti = MRecalculate (supersharp, vmulti, overlap=2, blksize=4, tr=tr, thsad=thsad/2, **mrecalculate_args)
return vmulti

clp = rule6
clp = core.fmtc.bitdepth(clp, bits=32, flt=True)
clp = padding(clp,36,36,36,36)
ref = core.bm3d.VBasic (clp, radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln = core.bm3d.VFinal (clp, ref,radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln0 = cln

vec = getvectors(cln)
blk = core.std.Expr(cln,"0.5")

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 32, 4, 12, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 16, 3, 10.5, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 8, 2, 9, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 4, 1, 7.5, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 2, 0, 6, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)
cln = freq_merge(cln0,cln,8)

cln.set_output()

[asarian]

[code]
import vapoursynth as vs
import mvmulti
core = vs.get_core()


You're the best! :) Thank you very much! Very illuminating!

[feisty2]

BUG REPORT

frames ∈ {[0, radius - 1] ∪ [last frame - radius +1, last frame]} are misplaced after VBasic/VFinal

[mawen1250]

Update r6

VAggregate bug fix: For Gray input and/or float output, frames [0, radius-1] are replaced by frame 'radius'. frames [num_frames - radius, num_frames - 1] are replaced by frame 'num_frames - radius - 1'
Re-licensed to MIT

[mawen1250]

Update r7

Update FFTW to 3.3.5

[MonoS]

Hi, i'm encountering a very strange artifact with this script
import vapoursynth as vs
import nnedi3_resample as edi

core = vs.get_core()

src = core.ffms2.Source("C:/U.S. Theatrical Trailer (Red Band).m2ts23.mkv")

blksize=16
overlap=8
pad = blksize + overlap

deint = core.fmtc.bitdepth(src, bits=16).std.CropRel(left=250, right=250)

deint = core.fmtc.resample(deint, deint.width+pad, deint.height+pad, sw=deint.width+pad, sh=deint.height+pad, kernel="point")

RGB = edi.nnedi3_resample(deint, deint.width, deint.height,csp=vs.RGB48, fast=False)

OPPF = core.bm3d.RGB2OPP(RGB, 1)
OPP = core.fmtc.bitdepth(OPPF, bits=16, flt=False, dmode=1, fulls=True, fulld=True)

super = core.mv.Super(OPP)

bvec2 = core.mv.Analyse(super, isb = True, delta = 2, blksize=blksize, overlap=overlap, truemotion=False)
bvec1 = core.mv.Analyse(super, isb = True, delta = 1, blksize=blksize, overlap=overlap, truemotion=False)
fvec1 = core.mv.Analyse(super, isb = False, delta = 1, blksize=blksize, overlap=overlap, truemotion=False)
fvec2 = core.mv.Analyse(super, isb = False, delta = 2, blksize=blksize, overlap=overlap, truemotion=False)

ref = core.mv.Degrain2(OPP, super, bvec1,fvec1,bvec2,fvec2, 425)
ref = core.fmtc.bitdepth(ref, flt=True)


flt = core.bm3d.VFinal(OPPF, ref, radius=1, matrix=100, sigma=[11,7,7]).bm3d.VAggregate(radius=1, sample=1)
"""
denf = core.bm3d.OPP2RGB(flt, sample=1)

den = core.fmtc.bitdepth(denf, bits=16, flt=False, dmode=0)

den = edi.nnedi3_resample(den[0:55], 946, 720, matd="709", fulld=False, csp=vs.YUV444P16, fulls=True, src_width=1420, src_height=1080, curves="709", curved="709", mats="RGB", sigmoid=True, kernel="spline64") + edi.nnedi3_resample(den[55::], 946, 720, matd="709", fulld=False, csp=vs.YUV444P16, fulls=True, src_width=1420, src_height=1080, curves="709", curved="709", mats="RGB", sigmoid=True, invks=True)
"""
flt.set_output()


The incriminated file is this one https://mega.nz/#!b14RDa6Z!82LdVsl0S4lfMJ5s7YH2pahJqFZSHvRjKk1sDqDiQ9U and the artifact can be seen at frame 648 https://abload.de/img/artifactgvq1k.png , [the screen was made with the last line uncommented] the more the luma sigma the more the artifact is visible.

Did i make some mistake in my script or did i find a bug in the implementation?

Thanks for the attention.

[royia]

Anyone encountered mawen1250 lately?

[dipje]

Click on name, view public profile, last activity is 12th November 2016.

I don't think doom9 was his primary forum btw, but not sure about stuff like that.

[Jindadil007]

import vapoursynth as vs
import mvmulti
core = vs.get_core()

fmtc_args = dict (fulls=True, fulld=True)
msuper_args = dict (chroma=False, hpad=32, vpad=32, sharp=2, levels=0)
manalyze_args = dict (search=3, chroma=False, truemotion=False, trymany=True, levels=0, badrange=-24)
mrecalculate_args = dict (chroma=False, truemotion=False, search=3, smooth=1)

def freq_merge (low, hi, p=8):
core = vs.get_core ()
Resample = core.fmtc.resample
MakeDiff = core.std.MakeDiff
MergeDiff = core.std.MergeDiff
def gauss (src):
upsmp = Resample (src, src.width * 2, src.height * 2, kernel="gauss", a1=100, **fmtc_args)
clip = Resample (upsmp, src.width, src.height, kernel="gauss", a1=p, **fmtc_args)
return clip
hif = MakeDiff (hi, gauss (hi))
clip = MergeDiff (gauss (low), hif)
return clip

def padding (src, left=0, right=0, top=0, bottom=0):
core = vs.get_core ()
Resample = core.fmtc.resample
w = src.width
h = src.height
clip = Resample (src, w+left+right, h+top+bottom, -left, -top, w+left+right, h+top+bottom, kernel="point", **fmtc_args)
return clip

def getvectors (src, pelclip=None, tr=6, pel=1, thsad=400):
core = vs.get_core ()
MSuper = core.mvsf.Super
MAnalyze = mvmulti.Analyze
MRecalculate = mvmulti.Recalculate
supersoft = MSuper (src, pelclip=pelclip, rfilter=4, pel=pel, **msuper_args)
supersharp = MSuper (src, pelclip=pelclip, rfilter=2, pel=pel, **msuper_args)
vmulti = MAnalyze (supersoft, overlap=16, blksize=32, tr=tr, **manalyze_args)
vmulti = MRecalculate (supersoft, vmulti, overlap=8, blksize=16, tr=tr, thsad=thsad/2, **mrecalculate_args)
vmulti = MRecalculate (supersharp, vmulti, overlap=4, blksize=8, tr=tr, thsad=thsad/2, **mrecalculate_args)
vmulti = MRecalculate (supersharp, vmulti, overlap=2, blksize=4, tr=tr, thsad=thsad/2, **mrecalculate_args)
return vmulti

clp = rule6
clp = core.fmtc.bitdepth(clp, bits=32, flt=True)
clp = padding(clp,36,36,36,36)
ref = core.bm3d.VBasic (clp, radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln = core.bm3d.VFinal (clp, ref,radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln0 = cln

vec = getvectors(cln)
blk = core.std.Expr(cln,"0.5")

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 32, 4, 12, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 16, 3, 10.5, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 8, 2, 9, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 4, 1, 7.5, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)

dif = core.std.MakeDiff(clp, cln)
dif = core.knlm.KNLMeansCL(dif, 0, 2, 0, 6, rclip=cln)
sup = core.mvsf.Super (dif, rfilter=2, pel=1, **msuper_args)
dif = mvmulti.DegrainN (blk, sup, vec, tr=6, thsad=10000)
cln = core.std.MergeDiff(cln, dif)
cln = freq_merge(cln0,cln,8)

cln.set_output()


Hi... Can you please inform where to import the video source...a stupid but newbie's question... My current working script is :

import vapoursynth as vs
import mvsfunc as mvf
import mvmulti
core = vs.get_core()
core.max_cache_size = 12000
video = core.d2v.Source(r'C:\DVD\Sample.d2v')
video = mvf.BM3D(video, sigma=[3,3,3], radius2=0)
video.set_output()

Thanks !!!

[Are_]

Hi... Can you please inform where to import the video source...

From feisty2's post, this line:
clp = rule6

rule6 is supposed to be your source filter, but if you are a newbie I don't think using fesity's scripts is a good idea.

[Jindadil007]

From feisty2's post, this line:
clp = rule6

rule6 is supposed to be your source filter, but if you are a newbie I don't think using fesity's scripts is a good idea.

Thanks Are... I am experimenting a bit and learning...
However when I am trying to do basic and final filtering...I am getting this error :

Error: bm3d.Basic: input clip: sub-sampled format is not supported when chroma is processed, convert it to YUV444 or RGB first. For the best quality, RGB colorspace is recommended as input.

This is the script I am trying :

import vapoursynth as vs
import mvsfunc as mvf
import mvmulti
import havsfunc as haf
core = vs.get_core()
core.max_cache_size = 20000
video = core.d2v.Source(r'C:\DVD\Sample.d2v')
pre = haf.sbr(video, 3)
ref = core.bm3d.Basic(video, pre, sigma=10)
flt = core.bm3d.Final(video, ref, sigma=10)
video.set_output()

can you help me with error... I am trying to use a better denoise alogrithm to improve the video quality with maximum details... Thanks !!!

[Are_]

You have to do what the error tels you to do, convert to RGB for example:

clip = core.resize.Bicubic(clip, format=vs.RGB48)

But anyway you are better using mvsfunc for that, that function greatly simplifies using bm3d, because bm3d can be quite annoying to use without it. You will not get better quality by not using mvsfunc.

You can read the comments on that module for more options (the comments inside the .py file, not the ones from the readme).

[Jindadil007]

You have to do what the error tels you to do, convert to RGB for example:

clip = core.resize.Bicubic(clip, format=vs.RGB48)

But anyway you are better using mvsfunc for that, that function greatly simplifies using bm3d, because bm3d can be quite annoying to use without it. You will not get better quality by not using mvsfunc.

You can read the comments on that module for more options (the comments inside the .py file, not the ones from the readme).
Many Thanks Are for the helpful tip...i'll study tomorrow and experiment...

Sent from my GT-N8000 using Tapatalk

[Jindadil007]

You have to do what the error tels you to do, convert to RGB for example:

clip = core.resize.Bicubic(clip, format=vs.RGB48)

But anyway you are better using mvsfunc for that, that function greatly simplifies using bm3d, because bm3d can be quite annoying to use without it. You will not get better quality by not using mvsfunc.

You can read the comments on that module for more options (the comments inside the .py file, not the ones from the readme).

Hi converted the colour format...now this is the error...

vapoursynth.Error: bm3d.VFinal: input clip and clip "ref" must be of the same format

Here is my script
clp = core.d2v.Source(r'C:\DVD\Sample.d2v')
clp = core.fmtc.bitdepth(clp, bits=32, flt=True)
clp = core.resize.Bicubic(clp, format=vs.RGB48)
ref = core.bm3d.VBasic (clp, radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln = core.bm3d.VFinal (clp, ref,radius=6, sigma=24.0, block_size=8, block_step=8).bm3d.VAggregate(6, 1)
cln0 = cln

Pl. guide me...

[feisty2]

rgb48 -> rgbs

[feisty2]

bm3d.VAggregate(6, sample=1)
sample = 1 is for floating point stuff, rgb48 is an integer format, read the freaking doc

[Jindadil007]

Why vBM3d is so slow as compared to fft3d filter. I tested a video with both filters. With vBm3d i get around 3-5 fps while in fft3d i get around 9-10 fps. A 2 pass encode with VBm3d for a 3 hour movie would take around 50 hours while fft3d takes around 15 hours. My machine : core i7 6700k, 16gb DDR4, 4 GB nvidia GTX 960, 500 gb SSD.

[feisty2]

Why vBM3d is so slow as compared to fft3d filter. I tested a video with both filters. With vBm3d i get around 3-5 fps while in fft3d i get around 9-10 fps. A 2 pass encode with VBm3d for a 3 hour movie would take around 50 hours while fft3d takes around 15 hours. My machine : core i7 6700k, 16gb DDR4, 4 GB nvidia GTX 960, 500 gb SSD.

I'll make it simple to you with a metaphor
complexity of fft3d: 1+1
complexity of bm3d: ∫∫∫f(x, y, z)dxdydz

denoising quality of fft3d: 10/100
denoising quality of bm3d: 95/100

I tested a video with both filters. With vBm3d i get around 3-5 fps while in fft3d i get around 9-10 fps.
that's very unlikely, bm3d should be at least 100 times slower with proper settings, something has to be wrong cuz bm3d is working way too fast.

[KingLir]

Hi all, how can I build BM3D on macOS ?
I have installed vapoursynth using brew and everything working fine but I get this:

$ ./configure
CXXFLAGS/LDFLAGS checking...
warning: pkg-config or pc files not found, lib detection may be inaccurate.
checking for vapoursynth headers...
warning: pkg-config or pc files not found, header detection may be inaccurate.
error: vapoursynth checking failed.
error: VapourSynth.h might not be installed.

[groucho86]

I really recommend you install Vapoursynth from github - I've had similar issues attempting to you use the brew installation.