Residual Correlation Regularization Based Image Denoising

Baloch, Gulsher; Özkaramanlı, Hüseyin; Yu, Runyi

doi:10.1109/lsp.2017.2789018

Cited by 15 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, as far as popular regularisation terms satisfying (A2),(A3),(A5) are concerned, we mention the Tikhonov (TIK) regulariser, which, in its general form reads G(Lx) = Lx − v 2 2 for suitably chosen L and v ∈ R N reflecting any prior knowledge on the solution. When L = D, the discrete image gradient, such regulariser is commonly referred to as discrete Sobolev regularisation and it is typically adopted when the image of interest is characterised by smooth regions.…”

Section: Proposition 2 If the Assumptions (A1)-(a2) Above Are Fulfill...mentioning

confidence: 99%

“…where R : R N → R + is a possibly non-convex and nonsmooth regularisation term, and where the data fidelity term (1/2) SKx−b 2 2 encodes the AWGN assumption on e, while the regularisation parameter μ > 0 balances the action of the fidelity against regularisation.…”

Section: Introductionmentioning

confidence: 99%

“…of the noise-is an effective idea as on the one hand it does not require to know the noise standard deviation and on the other it exploits much more information than the one encoded in the first moments of the noise distribution. Variational models for image denoising and deblurring containing data fidelity terms or regularisers aimed to explicitly enforce the residual whiteness property have been proposed; see, e.g., [2,18,20,21,27]. The good results achieved by such models demonstrate the potentiality of using the noise whiteness property.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems

et al. 2022

View full text Add to dashboard Cite

We propose an automatic parameter selection strategy for the single image super-resolution problem for images corrupted by blur and additive white Gaussian noise with unknown standard deviation. The proposed approach exploits the structure of both the down-sampling and the blur operators in the frequency domain and computes the optimal regularisation parameter as the one optimising a suitably defined residual whiteness measure. Computationally, the proposed strategy relies on the fast solution of generalised Tikhonov $$\ell _2$$ ℓ 2 –$$\ell _2$$ ℓ 2 problems as proposed in Zhao et al. (IEEE Trans Image Process 25:3683–3697, 2016). These problems naturally appear as substeps of the Alternating Direction Method of Multipliers used to solve single image super-resolution problems with non-quadratic, non-smooth, sparsity-promoting regularisers both in convex and in non-convex regimes. After detailing the theoretical properties allowing to express the whiteness functional in a compact way, we report an exhaustive list of numerical experiments proving the effectiveness of the proposed approach for different type of problems, in comparison with well-known parameter selection strategies such as, e.g., the discrepancy principle.

show abstract

Section: Proposition 2 If the Assumptions (A1)-(a2) Above Are Fulfill...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Baloch et al developed a RESI correlation regularization de-noising scheme that minimizes the correlation between neighboring RESI patches. 14 Roychowdhury et al estimated noise in chest CT image data with varying image quality using RESI. 15 To estimate white Gaussian noise in images, a work surveyed six methods and found that the noise estimation using the standard deviation measurement in RESI was most reliable.…”

Section: Introductionmentioning

confidence: 99%

A Residual Based Adaptive Image Sharpening Scheme

Lu¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

The sharpening is a scheme applied to highlight the intensity transitions in an image. This enhancement increases edge acutance and significantly improves the overall sharpness of processed images. Image sharpening is of enormous importance in medicine for diagnosis. Noise overshoot and over-sharpening effects occur in classical sharpening algorithms. The adaptive approach skips the noise producing a better sharpening scheme. Abrupt pixel value changes, if adequately emphasized, can be utilized effectively in the sharpening process. Since the residual image contains noise and edges, the bigger the filter size the greater the number of edges left in the residual image. Two residual images are produced first with different filter sizes. There is a relationship between these two residuals. A simple regression and standard deviation (±σ) is measured using these residuals. This linear equation is an expectation and the standard deviation is the difference between two residuals. One deviation (±σ) is proposed as the threshold. The points contained edges if the residual value is measured using a larger sized filter off ± σ than those residual using a smaller sized filter. A sensitive filter is adaptively applied to the image on those locations. It has been demonstrated that the proposed approach yields better results than the global filter as well as previous work on the P calculation results. A sharpening scheme without latent image noise amplification is useful for pattern recognition and machine-learning. In the future this scheme will be used to pre-process image segmentation.

show abstract

“…Xiao Bai et al [30] proposed a novel hyperspectral image denoising method based on tucker decomposition to model the nonlocal similarity across the spatial domain and the global similarity along the spectral domain. Gulsher Baloch et al [31] proposed a new residual correlation-based regularization for image denoising. The regularization can effectively render residual patches as uncorrelated as possible.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving

Jia

Meng

et al. 2018

Symmetry

View full text Add to dashboard Cite

The spatial-based method has become the most widely used method in improving the visibility of images. The visibility improving is mainly to remove the noise in the image, in order to trade off denoising and detail maintaining. A novel adaptive non-local means-based nonlinear fitting method is proposed in this paper. Firstly, according to the smoothness of the intensity around the central pixel, eight kinds of templates with different precision are exploited to approximate the central pixel through a novel adaptive non-local means filter design; the approximate weight coefficients of templates are derived from the approximation credibility. Subsequently, the fractal correction is used to smooth the denoising results. Eventually, the Rockafellar multiplier method is employed to generalize the smooth plane fitting to any geometric surface, thus yielding the optimal fitting of the center pixel approximation. Through a large number of experiments, it is clearly elucidated that compared with the classical spatial iteration-based methods and the recent denoising algorithms, the proposed algorithm is more robust and has better effect on denoising, while keeping more original details during denoising.

show abstract

Residual Correlation Regularization Based Image Denoising

Cited by 15 publications

References 16 publications

ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems

ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems

A Residual Based Adaptive Image Sharpening Scheme

A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving

Contact Info

Product

Resources

About