A novel de-interlacing method based on locally-adaptive Nonlocal-means

Dehghannasiri, Roozbeh; Shirani, Shahram

doi:10.1109/acssc.2012.6489324

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…But NLM still leaves "method noise" in the process, which is more like "white noise" [13]. Our proposed MFNR Algorithm doesn't leave such noise because it is only estimating the same pixel and doesn't take into account the surrounding pixels.…”

Section: Related Workmentioning

confidence: 99%

MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE

Nazir¹,

Younis²,

Shahzad³

2020

Preprint

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In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

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Section: Related Workmentioning

confidence: 99%

MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE

Nazir¹,

Younis²,

Shahzad³

2020

Preprint

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show abstract

“…• Block matching based motion estimation 11,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] • Edge direction dependent block based interpolation [35][36][37][38][39][40][41] • Edge direction dependent interpolation for example with adaptive distance weighting or based on second order image derivatives [42][43][44][45][46] • Gradient analysis with covariance matrices or tensors 47,48 • Using adaptive NL-means, bilateral or trilateral filters [49][50][51][52][53] • Dynamic time warping 54…”

Section: Related Workmentioning

confidence: 99%

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Müller

2016

SPIE Proceedings

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The necessity to process interlaced images in surveillance, reconnaissance, or further computer vision areas should be a topic of the past. But, for different reasons, it is not. So, there are situations in practice, in which interlaced images have to be processed. Since a lot of algorithms strongly degrade when working with such images directly, a usual method is to double or interpolate image lines in order to discard one of the two enclosed image frames. This is efficient but leads to weak results, in which half of the original information is lost. Alternatively, a lot of valuable computation time has to be spent to solve the highly complex motion compensation task in order to improve the results significantly. In this paper, an efficient algorithm is presented to solve this dilemma. First, the algorithm solves the complex 2-D mapping problem using the best state-of-the art optical flow method that could be found for this purpose. But, of course, for different physical reasons there are regions which cannot properly be handled by optical flow by itself. Therefore, an efficient post-processing method detects and removes remaining artifacts afterwards, which is the main contribution of this paper. This method is based on color interpolation incorporating local image structure. The presented results document the overall performance of the approach with respect to obtained image quality and calculation time. The method is easy to implement and offers a valuable pre-processing for a lot of computer vision tasks

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“…If compared with other well-known denoising techniques, such as the Gaussian smoothing model, the anisotropic diffusion model, the total variation denoising, the neighborhood filters and an elegant variant, the Wiener local empirical filter, the translation invariant wavelet thresholding, the non-local means method noise looks more like white noise [5]. Recently non-local means has been extended to other image processing applications such as deinterlacing [6] and view interpolation [7].…”

Section: Introductionmentioning

confidence: 99%

Study on Image Noise Reduction Algorithm at Improved NL Means Based on Color Information

Ma¹,

Zhang²,

Zhang³

2018

dtcse

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For the random noise of digital image processing is embedded in the old films, to study the improved Non Means algorithm in Local. Through the whole area of the surrounding pixels of gray distribution in contrast to the overall, according to the gray level distribution similarity obtained decision weights, to study and calculate the corresponding each pixel set color Information adaptive parameters, and then calculate the similarity, screened based on the template. In order to achieve the goal of eliminating the noise by modifying the value of the target pixel region, and using the weighted average value instead of the target pixel region value. Finally, the feasibility and the scientific nature of the noise reduction performance are simulated by referring to the image noise elimination precision and image quality parameters.

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A novel de-interlacing method based on locally-adaptive Nonlocal-means

Cited by 8 publications

References 12 publications

MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE

MFNR: Multi-Frame Method for Complete Noise Removal of all PDF Types in Multi-Dimensional Data Using KDE

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Study on Image Noise Reduction Algorithm at Improved NL Means Based on Color Information

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