An adaptive recursive 2-D filter for removal of Gaussian noise in images

Rank, K.; Unbehauen, R.

doi:10.1109/83.148617

Cited by 38 publications

(16 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gaussian filters are widely used to filter noisy 2-dimensional (2D) images. The filter's impulse response is a Gaussian function [27,28]. The relation of the support size of Gaussian kernel (k) and the standard deviation (σ) is here set to…”

Section: Spatial 2d Gaussian Filteringmentioning

confidence: 99%

Wavelet analysis of cardiac optical mapping data

Xiong

Nattel

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Spatial 2d Gaussian Filteringmentioning

confidence: 99%

Wavelet analysis of cardiac optical mapping data

Xiong

Nattel

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…Many denoising algorithms have been proposed in the past such as Gaussian filter [8], Adaptive image filter [9], Anisotropic diffusion (AD) filter [10]- [13], Doubly local Weiner filter [14], Sparse 3-D Transform Domain filter [15], adaptive median filter and their variants [16]- [20] to suppress image noise in diverse applications. Conventional averaging filters are generally performance limited, owing to the suppression of high frequency structure of the image, leading to the removal of finer details along with noise [8]. On the other hand, adaptive image filters tend to produce blurred edges due to iterations [9].…”

Section: Introductionmentioning

confidence: 99%

Rayleigh based bilateral filtering for flash and no-flash image pairs

Sinha¹,

Bhateja²,

Sharma³

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

View full text Add to dashboard Cite

Digital photography provides a quick and facile means to capture a pair of images i.e. flash and no-flash image. Digital images are often superimposed by noises at the acquisition stage depending upon the availability of lighting. Image denoising is a key technology to reduce the noise levels from the images while preserving the fine details and structures. Predominantly, conventional Bilateral filter is employed, providing better noise reduction capabilities with low noise density while preserving the edges. The spatial processing of pixels is reckoned by using a Gaussian function (symmetric and smoothly decaying function). This paper introduces a Bilateral filter which incorporates a Rayleigh distribution function in its domain filter to smoothen out the image pairs more effectively. This filter has been implemented on both the images (flash and no-flash) in the present work. Simulations are carried out on the images contaminated with different levels of Gaussian noise and are evaluated on the basis of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as image quality parameters. Performance of the proposed filter has shown significant results when compared to the conventional Bilateral filter.

show abstract

“…During the past few decades, many intelligent methods have been proposed to improve single-image denoising performance. From pixel level filtering methods, such as Gaussian filtering [1], bilateral filtering, and total variation regularization [2], to patch based filtering methods, such as non-local means [3], block-matching 3D filtering (BM3D) [4], and sparse representation [5], single-image based denoising performance has been greatly improved, with image details well recovered when the image is slightly noisy. As such filtering methods are widely used in computer vision, work has considered how to speed them up, e.g., for the bilateral filter [6] and weighted median [7].…”

Section: Introductionmentioning

confidence: 99%

Removing mixed noise in low rank textures by convex optimization

Liang

2016

Comp. Visual Media

View full text Add to dashboard Cite

This paper introduces a new low rank texture image denoising algorithm, which can restore low rank texture contaminated by both Gaussian and salt-and-pepper noise. The algorithm formulates texture image denoising in terms of solving a low rank matrix optimization problem. Simply assuming low rank is insufficient to describe the properties of natural images, causing high noise amplitudes which lead to unsatisfactory denoising results or serious loss of image details. Thus, in addition to the low rank assumption, the continuity of natural images is also assumed by the algorithm, by adding a total variation regularizer to the optimization objective function. We further give an effective algorithm to solve this optimization problem. By combining the low rank and continuity assumptions, the proposed algorithm overcomes the deficiencies of using either the low rank assumption or total variation regularization alone. Experiments show that our algorithm can effectively remove mixed noise in low rank texture images, and is better than existing algorithms in both its subjective visual effects and in terms of quantitative objective measures.

show abstract

An adaptive recursive 2-D filter for removal of Gaussian noise in images

Cited by 38 publications

References 7 publications

Wavelet analysis of cardiac optical mapping data

Wavelet analysis of cardiac optical mapping data

Rayleigh based bilateral filtering for flash and no-flash image pairs

Removing mixed noise in low rank textures by convex optimization

Contact Info

Product

Resources

About