Fast blur algorithms library for Rust

There are some very good and blazing fast algorithms that do blurring images. Best optimized for NEON and SSE.

You may receive gaussian blur in 100 FPS for 4K photo.

Much faster than image default blur.

When 4-channels mode is in use that always considered thad alpha channel is the last.

Performance

Most blur algorithms done very good and works at excellent speed. Where appropriate comparison with OpenCV is available. For measurement was used M3 Pro with NEON feature. On x86_84 OpenCV might be better sometimes since there are not AVX-2 support in library

Usage

cargo add libblur

Stack blur

The fastest with acceptable results. Result are quite close to gaussian and look good. Sometimes noticeable changes may be observed. However, if you'll use advanced analysis algorithms non gaussian methods will be detected. Not suitable for antialias. Results just a little worse than in 'fast gaussian', however it's faster.

O(1) complexity.

libblur::stack_blur( & mut bytes, stride, width0, height, radius, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in single-threaded mode with 77 radius.

	Time
libblur	43.58ms
OpenCV	89.64ms

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 77 radius.

	Time
libblur	8.68ms
OpenCV	87.99ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in multithreaded mode with 77 radius.

	Time
libblur	6.73ms
OpenCV	93.26ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in single-threaded mode with 77 radius.

	Time
libblur	31.18ms
OpenCV	90.82ms

Fast gaussian

Very fast. Result are quite close to gaussian and look good. Sometimes noticeable changes may be observed. However, if you'll use advanced analysis algorithms non gaussian methods will be detected. Not suitable for antialias. Do not use when you need gaussian. Based on binomial filter, generally speed close, might be a little faster than stack blur ( except NEON or except non multithreaded stack blur, on NEON much faster or overcome non multithreaded stackblur ), however results better as I see. Max radius ~320 for u8, for u16 will be less.

O(1) complexity.

libblur::fast_gaussian(& mut bytes, stride, width0, height, radius, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in single-threaded mode with 77 radius.

	Time
libblur	47.40ms
OpenCV	-

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 77 radius.

	Time
libblur	9.95ms
OpenCV	-

Example comparison time for blurring image 2828x4242 RGBA 8-bit in multithreaded mode with 77 radius.

	Time
libblur	9.74ms
OpenCV	--

Example comparison time for blurring image 2828x4242 RGBA 8-bit in single-threaded mode with 77 radius.

	Time
libblur	43.60ms
OpenCV	--

Fast gaussian next

Very fast. Produces very pleasant results close to gaussian. If 4K photo blurred in 10 ms this method will be done in 15 ms. Max radius ~150-180 for u8, for u16 will be less.

O(1) complexity.

libblur::fast_gaussian_next( & mut bytes, stride, width, height, radius, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in single-threaded mode with 77 radius.

	Time
libblur	53.99ms
OpenCV	-

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 77 radius.

	Time
libblur	10.26ms
OpenCV	-

Tent blur

2 sequential box blur. Medium speed, good-looking results.

O(1) complexity.

libblur::tent_blur(bytes, stride, & mut dst_bytes, stride, width, height, radius, FastBlurChannels::Channels3);

Median blur

Median blur ( median filter ). Implementation is fast enough.

O(log R) complexity.

libblur::median_blur(bytes, stride, & mut dst_bytes, stride, width, height, radius, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 35 radius.

	Time
libblur	468.47ms
OpenCV	725.89ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in multithreaded mode with 35 radius.

	Time
libblur	643.22ms
OpenCV	788.93ms

Gaussian blur

Excellent results. Have improvements, however, much slower than any approximations slow. Use when use need gaussian methods - smoothing, anti-alias, FFT analysis etc.

Kernel size must be odd. Will panic if kernel size is not odd.

O(R) complexity.

libblur::gaussian_blur( & bytes, src_stride, & mut dst_bytes, dst_stride, width, height, kernel_size, sigma, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 151 kernel size.

	Time
libblur	171.81ms
OpenCV	251.10ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in multithreaded mode with 151 kernel size.

	Time
libblur	172.21ms
OpenCV	304.40ms

Gaussian box blur

generally 3 sequential box blurs it is almost gaussian blur, slow, really pleasant results. Medium speed.

O(1) complexity.

libblur::gaussian_box_blur(bytes, stride, & mut dst_bytes, stride, width, height, radius, FastBlurChannels::Channels3);

Box blur

Box blur. Compromise speed with bad looking results. Medium speed.

O(1) complexity.

libblur::box_blur(bytes, stride, & mut dst_bytes, stride, width, height, radius, FastBlurChannels::Channels3);

Example comparison time for blurring image 3000x4000 RGB 8-bit in multithreaded mode with 77 radius.

	Time
libblur	14.08ms
OpenCV	96.41ms

Example comparison time for blurring image 3000x4000 RGB 8-bit in single-threaded mode with 77 radius.

	Time
libblur	57.47ms
OpenCV	92.66ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in multithreaded mode with 77 radius.

	Time
libblur	12.79ms
OpenCV	136.66ms

Example comparison time for blurring image 2828x4242 RGBA 8-bit in single-threaded mode with 77 radius.

	Time
libblur	51.90ms
OpenCV	134.28ms