Dogway's Filters Packs

[tormento]

Originally Posted by Dogway

If the blurring was too extreme I would recommend the model HurrDeblur

With what AVS+ plugin?

[poisondeathray]

Originally Posted by tormento

With what AVS+ plugin?

There is only 1 plugin that can be used directly in avs+ for HurrDeblur - avs-mlrt

https://github.com/Asd-g/avs-mlrt

[tormento]

Originally Posted by poisondeathray

There is only 1 plugin that can be used directly in avs+ for HurrDeblur - avs-mlrt

My poor 1660 Super will die on fire.

[simple_simon]

I've been using removegrain(mode=5) or vsdegrainmedian(modeY=3) to help with compressability without changing the overall look of the picture too much. What's the best ex_median mode to use as an equivalent?

[Lucky38]

hi,

I'm looking option to denoise with smdegrain enitre frame exept area where is the motion. How could i achive that?

[tormento]

@Dogway

Is there a way to pass parameters to prefilters but to use it as external prefilter? I am interested mostly in 6,7,8.

[DTL]

Originally Posted by Lucky38

hi,

I'm looking option to denoise with smdegrain enitre frame exept area where is the motion. How could i achive that?

If SMDegrain exports MVs clip (as some named global ?) - you can feed it to MMask() and use output mask with AVS filters like Overlay() or Layer().
If not - you need to calculate MVs again (MSuper+MAnalyse).


After looking into ex_median - it is a great 64 different median (median-like ?) modes ! Now I am in the process of testing some ways of averaging MVs gathered from 'area' searches (with some offset from block's center position in tessellation grid - it sort of very dense overlap with possible steps on 1 sample dx,dy) to make more stable MV for degraded by noise sources. Which of the 64 different median modes can be tested for this process ?

The gathered MVs from AreaMode search can be treated either as 1D array or as 2D array. Currently, the implemented averaging modes process as 1D array and not use possible 2D information gathered (like distance/radius from block's center of the search position). The offset distance from block's center makes positives and negatives:
1. Positives - the more offset distance from block's center - the more new (not tested with original block's position) samples used and gather more new information about motion. It is sort of an increasing block size.
2. Negatives - the more offset distance from block's center - the more image motion may be changed and the offsetted MVs may be really somehow different from the original block's position.

So with 1 and 2 I am still not sure if we can apply some '2D spatial weighting' to the averaging process. Anyway AreaMode gathers lots of new valuable data to process about motion in some close area about block's center (not only MVs but also SAD (or other supported dissimilarity metric)) it is 2D array of 3 values in each (of size 2x2 + center or 3x3 + center minimum with AreaMode 'radius' of 1 sample and many more with > 1). So lots of different new processing modes may be tested to find the best way of refining MVs using new gathered data.

2D view of the question:


For gathered MVs set (2D array of scanned to 1D) of MV(0,0) and some of the MV(dx, dy) members (where dx and dy are +-integer offsets in the units of current search level of MAnalyse: +-1 sample at the full-pel level for example) which of the ex_median() averaging algorithms may be recommended to test to get more stable output MV ?

Each MV() is a structure of 3 members (dx, dy, sad). Where dx and dy are relative motion vector X and Y components). So for analysys of 2 completely equal frames MV(0,0) = MV(dx,dy)= (dx=0,dy=0,sad=0).
But if frames have some noise added: MV(0,0) may be not equal to any of others MV(dx, dy). Also MV(0,0) may have non-zero dx, dy also.

[Lucky38]

Originally Posted by DTL

If SMDegrain exports MVs clip (as some named global ?) - you can feed it to MMask() and use output mask with AVS filters like Overlay() or Layer().
If not - you need to calculate MVs again (MSuper+MAnalyse).

@DTL,

Have you got some example how should look like proper call with SMDegrain?

Many Thanks

[DTL]

Documentation https://raw.githack.com/Dogway/Avisy...SMDegrain.html
says
Motion Vectors Globals Input/Output: Reuse motion vectors globals for faster processing, or just use SMDegrain() as a shortcut for creating nice quality motion vectors.

Example

                         SMDegrain(tr=3,thSAD=400,globals=3)    # Outputs vectors

                         Super = MSuper(levels=1)               # Add this line just before if you have some processing between Globals Output and Input.
                         MDegrain3(Super, bVec1, fVec1, bVec2, fVec2, bVec3, fVec3, thSAD=400)

So you can try

denoised=SMDegrain(tr=3,thSAD=400,globals=3)    # Outputs vectors
mask_clip=MMask(fVec1)
Overlay(last, denoised, mask=mask_clip)

I can not check because SMDegrain too complex for me and I can not gather all required dependencies and install as required. You need to check mask polarity (Use Invert() or arguments replacement in reverse order in Overlay() if result is inverted) and set mask 'sensitivity' to motion adjusting ml param at MMask(). Check mask_clip view to see how it masks motion areas,

[Lucky38]

Originally Posted by DTL

Documentation https://raw.githack.com/Dogway/Avisy...SMDegrain.html
says
Motion Vectors Globals Input/Output: Reuse motion vectors globals for faster processing, or just use SMDegrain() as a shortcut for creating nice quality motion vectors.

Example

                         SMDegrain(tr=3,thSAD=400,globals=3)    # Outputs vectors

                         Super = MSuper(levels=1)               # Add this line just before if you have some processing between Globals Output and Input.
                         MDegrain3(Super, bVec1, fVec1, bVec2, fVec2, bVec3, fVec3, thSAD=400)

So you can try

denoised=SMDegrain(tr=3,thSAD=400,globals=3)    # Outputs vectors
mask_clip=MMask(fVec1)
Overlay(last, denoised, mask=mask_clip)

I can not check because SMDegrain too complex for me and I can not gather all required dependencies and install as required. You need to check mask polarity (Use Invert() or arguments replacement in reverse order in Overlay() if result is inverted) and set mask 'sensitivity' to motion adjusting ml param at MMask(). Check mask_clip view to see how it masks motion areas,

I need to test this..