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Project: MSU Digital TV Signal Enhancement

MSU Graphics & Media Lab (Video Group)

Project:	Dr. Dmitriy Vatolin
Implementation, ideas:	Maxim Makhinya
Implementation:	Kalinkina Daria

In this project a new fast and flexible algorithm for image denoising was developed. It could be easily used to enhance image and video corrupted by strong compression. This technique can be adapted to use in de-blocking and Gibbs phenomenon reduction algorithms (de-blocking and de-ringing algorithms). Since the fastest way to reduce noise in an image is averaging pixels using only neighbor pixels which are similar in some way to the current pixel, we designed a new method of averaging that surpasses already known methods in visual quality as well as in speed. One of the advantages of our method is the usage of the local window of a small radius to reduce small or middle strength noise, another one is a simple measure for pixels' similarity determination which can be used in both spatial and motion compensation-based temporal denoising methods. During the project we also showed how proposed technique can be optimized to reduce number of computationally expensive operations for using it on DSP-like processors.

Results

We have tested our fast variant of spatial noise reduction filter by applying it to different color videos after adding Gaussian noise of different levels. Such a procedure allows comparing denoised video to original one. We use standard PSNR measure to estimate denoising quality, for each test video sequence this measure was calculated for every frame and then averaged. Average results of comparison with other well-known spatial algorithms are presented bellow. We use six different video sequences and three different noise levels for each of them. First three-five letters in the test sequences’ names in figure below designate abbreviated name of a test sequence, and numbers in the test sequences’ names mean value of Gauss variance of added Gauss Zero Mean noise. Proposed method shows good results on all test sequences.

Numerical comparison of proposed method to other well-known algorithms

In addition to high PSNR values, proposed algorithm shows good visual quality.

Next example illustrates a source frame from “shattered” sequence, up-scaled part of it is used in visual comparison between different methods. This example proves that proposed method can efficiently suppress noise without decreasing overall image sharpness and over-blurring.

A frame from the "shattered" sequence

An up-scaled part of frame from “shattered” sequence with original noise	After proposed method
After Monte Carlo denoising	After FUZZY denoising
After KNN denoising	After FUELS denoising

The following two examples show how proposed method can be used for enhancement of images corrupted by strong compression.

Original frame

An up-scaled part of source frame with original artefacts

Recovered sample using proposed algorithm

This is a vivid example of how proposed method can be used in de-blocking applications. To achieve such results we set up denoising strength to large value.

Next example shows another useful property of developed method – it removes effects that are visually similar to Gibbs phenomenon, which can be often seen in strongly compressed images in areas around sharp edges. We also pay attention to another interesting property of the algorithm: example shows that processed image, besides being cleaned from artifacts, is even sharper than the original one. We have applied our method twice to obtain this result.

Original frame
An up-scaled part of source frame with original artefacts	Recovered sample using proposed algorithm

Acknowledgements

We would like to express our gratitude to Intel Corporation, that supported this work, and personally to Tiehan Lu and Walid Ali. Additional thanks to Computer Graphics and Multimedia at Moscow State University and personally to Bayakovski Yuri.

References

1. H.B. Mitchell and N. Mashkit, “Noise smoothing by a fast k-nearest neighbour algorithm” // Signal Processing: Image Communication, vol. 4, pp. 227–232, 1992.
2. Ruifeng Xu and Sumanta N. Pattanaik, “A Novel Monte Carlo Noise Reduction Operator” // IEEE Computer Graphics and Applications, 2005
3. C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images,” Proc. IEEE 6th Int’l Conf. Computer Vision (ICCV), IEEE CS Press, 1998, pp. 839-846.
4. Torsten Seemann and Peter Tischer, “Structure Preserving Noise Filtering of Images using Explicit Local Segmentation” // IEEE Pattern Recognition, 1998, 16-20 Aug, Vol. 2, pp.1610-1612
5. Dimitri Van De Ville, Mike Nachtegael, Dietrich Van der Weken, Etienne E. Kerre Wilfried Philips, and Ignace Lemahieu, “Noise Reduction by Fuzzy Image Filtering” // IEEE Transactions On Fuzzy Systems, Vol. 11, No. 4, August 2003 429

Download

This algorithm was developed in collaboration with Intel so currently it is unavailable due to contract limitations.

Other resources

Video resources:

Public MSU video filters Here are available VirtualDub and AviSynth filters. Commonly we develop a whole family of some kind of a filter. Generally there are also versions optimized for PC and hardware implementations (ASIC/FPGA/DSP). These optimized versions can be licensed to companies. Please contact us for details via video(at)graphics.cs.msu_ru. MSU Cartoon Restore (New!) MSU Cartoon Restore (New!) MSU Noise Estimation (New!) MSU Frame Rate Conversion (New!) MSU Image Restoration (New!) MSU Denoising (Top!) MSU Old Cinema MSU Deblocking (Top!) MSU Smart Brightness and Contrast (Top!) MSU Smart Sharpen (Top!) MSU Noise generation MSU Noise estimation MSU Motion Estimation Information MSU Subtitles removal MSU Logo removal (Top!) MSU Deflicker (Top!) MSU Field Shift Fixer AviSynth plug-in MSU StegoVideo MSU Cartoonizer (Top!) MSU SmartDeblocking (Top!) MSU Color Enhancement MSU Old Color Restoration MSU filters FAQ (Read me!) MSU filters statistics	MSU filters for companies We are working with Intel, Samsung, RealNetworks and other companies on adapting our filters other video processing algorithms for specific video streams, applications and hardware like TV-sets, graphics cards, etc. Some of such projects are non-exclusive. Also we have internal researches. Please let us know via video(at)graphics.cs.msu_ru if you are interested in acquiring a license for such filters or making a custom R&D project on video processing, compression, computer vision. Automatic Objects Segmentation (New!) Semiautomatic Objects Segmentation New Frame Rate Conversion (New!) New Deinterlacer (New!) MSU-Samsung Deinterlacing Project Digital TV Signal Enhancement Old Film Recovery Tuner TV Restore Panorama Video2Photo SuperResolution SuperPrecision MSU-Samsung image and video resampling MSU-Samsung Frame Rate Conversion Motion Phase filter Deshaker (video stabilization) Film Grain/Degrain filter Deblurring filter Video Content Search
Codecs comparisons Objective and subjective quality evaluation tests for video and image codecs 4-th MPEG4-AVC/H.264 Comparison (New!) Lossless Video Codecs Comparison 2007 (New!) 3-rd MPEG4-AVC/H.264 Comparison HD Photo and JPEG 2000 Comparison MPEG-2 Decoders Crash Test (Top!) Subjective Compar. of 4 Modern Codecs (Top!) 2-nd MPEG-4 AVC/H.264 Comparison (Top!) FAQ of 2-nd H.264 Comparison 1-st MPEG-4 AVC/H.264 Codecs Comparison FAQ of 1-st H.264 Comparison JPEG 2000 Image Codecs Comparison (Top!) Lossless Video Codecs Comparison 2004 MPEG-4 SP/ASP Codecs Comparison (Top!) Video codecs comparison (old) Ext. link: x264 parameters efficiency comparison	Video quality metrics Programs with different objective and subjective video quality metrics implementation MSU Video Quality Metric - objective metrics for codecs and filters comparison (Top!) Implemented metrics short info: PSNR, Delta, MSAD, MSE, SSIM, VQM, MSU Blurring, MSU Blocking MSU Brightness Flicking Metric MSU Brightness Independent PSNR MSU Drop Frame Metric MSU Noise Estimation Metric MSU VQM Metric Tool FAQ MSU Human Perceptual Quality Metric - several metrics for exact visual tests (Top!) Implemented metrics short info: ITU-R BT.500-11: DSIS, DSCQS I & II, SCACJ; EBU: SAMVIQ; MSUCQE MSU PVQ Metric Tool FAQ
Video codecs projects Different research and development projects on video codecs MSU Lossless Video Codec (Top!) MSU Screen Capture Lossless Codec (Top!) MSU MPEG-2 Video Codec x264 Codec Improvement	Other Other information Crazy gallery (filters screams :) License for commercial usage of MSU VideoGroup Public Software (please be careful: some soft like metrics has another license!)

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Last updated: 07-December-2007

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