Learning How to Combine Internal and External Denoising Methods

Burger, Harold Christopher; Schuler, Christian J.; Harmeling, Stefan

doi:10.1007/978-3-642-40602-7_13

Cited by 57 publications

(63 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combining for instance, non-local principles with spectral decomposition [37], or BM3D with neural networks [4]. This allows one to mix different denoising principles, to improve the image quality.…”

Section: 52%mentioning

confidence: 99%

Data Adaptive Dual Domain Denoising: a Method to

Pierazzo¹,

Facciolo²

2017

Image Processing on Line

View full text Add to dashboard Cite

This article presents DA3D (Data Adaptive Dual Domain Denoising), a "last step denoising" method that takes as input a noisy image and as a guide the result of any state-of-the-art denoising algorithm. The method performs frequency domain shrinkage on shape and dataadaptive patches. DA3D doesn't process all the image samples, which allows it to use large patches (64 × 64 pixels). The shape and data-adaptive patches are dynamically selected, effectively concentrating the computations on areas with more details, thus accelerating the process considerably. DA3D also reduces the staircasing artifacts sometimes present in smooth parts of the guide images. The effectiveness of DA3D is confirmed by extensive experimentation. DA3D improves the result of almost all state-of-the-art methods, and this improvement requires little additional computation time. Source CodeThe C++ source code, the code documentation, and the online demo are accessible at the IPOL web page of this article web site 1 Compilation and usage instruction are included in the README.txt file of the archive.

show abstract

Section: 52%mentioning

confidence: 99%

Data Adaptive Dual Domain Denoising: a Method to

Pierazzo¹,

Facciolo²

2017

Image Processing on Line

View full text Add to dashboard Cite

show abstract

“…The approach by Burger et al propose to use learning to combining denoising results from internal and external results [28]. The method in [31] uses web images to recover correlated images and use external patches in BM3D.…”

Section: External-based Denoisingmentioning

confidence: 99%

Low-Rankness Transfer for Realistic Denoising

Badri

Yahia

Aboutajdine

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-Current state-of-the-art denoising methods such as non-local low-rank approaches have shown to give impressive results. They are however mainly tuned to work with uniform Gaussian noise corruption and known variance, which is far from the real noise scenario. In fact, noise level estimation is already a challenging problem and denoising methods are quite sensitive to this parameter. Moreover, these methods are based on shrinkage models that are too simple to reflect reality, which results in over-smoothing of important structures such as small-scale text and textures. We propose in this paper a new approach for more realistic image restoration based on the concept of low-rankness transfer (LRT). Given a training clean/noisy image pair, our method learns a mapping between the non-local noisy singular values and the optimal values for denoising to be transfered to a new noisy input. One single image is enough for training the model and can be adapted to the noisy input by taking a correlated image. Experiments conducted on synthetic and real camera noise show that the proposed method leads to an important improvement both visually and in terms of PSNR/SSIM.

show abstract

“…Luo et al 18 proposed a data-dependent denoising procedure to restore noisy image and leverages the similarity of the external database, showing the superiority of the new algorithm over existing methods. A learning-based approach using a neural network, combining denoising results from an internal method and an external method, was proposed by Burger et al 19 Low-rank technique, 20 which is formulated as a minimization problem where the cost function measures the fit between a given matrix (the original data) and an approximating matrix (the optimization data), is subject to a constraint that the approximating matrix has reduced rank. The fact that a clean image has a low-rank matrix and rank of the noisy image is much larger than the clean image is in-line with the above introduction, which can be utilized in image denoising.…”

Section: Introductionmentioning

confidence: 99%

Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image

et al. 2017

View full text Add to dashboard Cite

Abstract. Image denoising is a crucial step before performing segmentation or feature extraction on an image, which affects the final result in image processing. In recent years, utilizing the self-similarity characteristics of the images, many patch-based image denoising methods have been proposed, but most of them, named the internal denoising methods, utilized the noisy image only where the performances are constrained by the limited information they used. We proposed a patch-based method, which uses a low-rank technique and targeted database, to denoise the optical coherence tomography (OCT) image. When selecting the similar patches for the noisy patch, our method combined internal and external denoising, utilizing the other images relevant to the noisy image, in which our targeted database is made up of these two kinds of images and is an improvement compared with the previous methods. Next, we leverage the low-rank technique to denoise the group matrix consisting of the noisy patch and the corresponding similar patches, for the fact that a clean image can be seen as a low-rank matrix and rank of the noisy image is much larger than the clean image. After the firststep denoising is accomplished, we take advantage of Gabor transform, which considered the layer characteristic of the OCT retinal images, to construct a noisy image before the second step. Experimental results demonstrate that our method compares favorably with the existing state-of-the-art methods.

show abstract

Learning How to Combine Internal and External Denoising Methods

Cited by 57 publications

References 14 publications

Data Adaptive Dual Domain Denoising: a Method to

Data Adaptive Dual Domain Denoising: a Method to

Low-Rankness Transfer for Realistic Denoising

Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image

Contact Info

Product

Resources

About