Non-local Means using Adaptive Weight Thresholding

Khan, Asif; El-Sakka, Mahmoud R.

doi:10.5220/0005787100670076

Cited by 3 publications

(1 citation statement)

References 19 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lai and Dou [35] introduced an improved neighborhood pre-classification strategy for optimized weight kernels of NL-Means filter. Khan and El-Sakka [32] introduced a variant of the NL-Means scheme by using a thresholding step to reduce the number of similar patches before weight averaging the patches.…”

Section: Averaging Patch-based: Non-local Meansmentioning

confidence: 99%

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani

El-Sakka

2017

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.Keywords: Patch-based image denoising, Bilateral filter, Non-local means filtering, Probabilistic patch-based filtering, Dictionary learning filtering, K-SVD, Gaussian patch-PCA filtering, BM3D Review IntroductionThe noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based *Correspondence: elsakka@csd.uwo.ca 2 Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, N6A 5B7, London, Ontario, Canada Full list of author information is available at the end of the article image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or "blocks, " which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches.

show abstract

Section: Averaging Patch-based: Non-local Meansmentioning

confidence: 99%