Computational geometry.

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.

Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression

A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN

In this article, the authors propose an adaptive frequency median filter (AFMF) to remove the salt and pepper noise. AFMF uses the same adaptive condition of adaptive median filter (AMF). However, AFMF employs frequency median to restore grey values of the corrupted pixels instead of the median of AMF. The frequency median can exclude noisy pixels from evaluating a grey value of the centre pixel of the considered window, and it focuses on the uniqueness of grey values. Hence, the frequency median produces a grey value closer to the original grey value than the one by the median of AMF. Therefore, AFMF outperforms AMF. In experiments, the authors tested the proposed method on a variety of natural images of the MATLAB library, as well as the TESTIMAGES data set. Additionally, they also compared the denoising results of AFMF to the ones of other state-of-the-art denoising methods. The results showed that AFMF denoises more effectively than other methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0398

Adaptive frequency median filter for the salt and pepper denoising problem

This paper discusses a novel algorithm for salt and pepper image noise cancelation using cardinal B-splines. The purpose of this paper is to present an analysis and application of cardinal B-splines for image noise cancellation. To apply the cardinal B-splines, one should analyze the different properties of the cardinal B-spline. Here we make use of the interpolation property and compact support of the cardinal B-splines. There are various assumptions and conditions that are considered while applying the cardinal B-splines for noise removal. The result of denoising the images affected up to 95 % of the salt and pepper noise has been shown. The results of proposed method are being compared with the other existing methods, and the comparison shows the better performance of our method.

A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines

In this paper, we propose a novel adaptive median-based lifting filter for image de-noising which has been corrupted by homogeneous salt and pepper noise. The median-based lifting filter removes the noise of the input image by calculating the median of the neighboring significant pixels. The algorithm for image noise removal uses the lifting scheme of the second-generation wavelets in conjunction with the proposed adaptive median-based lifting filter. The experimental results demonstrate the efficiency of the proposed method. The proposed algorithm is compared with all the basic filters, and it is found that our method outperforms many other algorithms and it can remove salt and pepper noise with a noise level as high as 90%. The algorithm works exceedingly well for all levels of noise, as illustrated in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) measures.

Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets

Progressive image transmission provides a convenient User Interface when images are transmitted slowly. In this paper, we present a progressive image reconstruction scheme based on the multi-scale edge representation of images. In the multi-scale edge representation an image is decomposed into Most Significant Points which represent the strong edges and Insignificant Points which represent weak edges. Image re-construction is done based on the approximation of image regarded as a function, by a linear spline over adapted Delaunay triangulation. The proposed method progressively improves the quality of the reconstructed image till the desired quality is obtained.

A Fast Progressive Image Transmission Algorithm Using Linear Bivariate Splines

The quality of Finger Print Images in image forensics plays a vital role in the the accuracy of biometric based identification and authentication system. To suppress the salt and pepper noise in fingerprint images, B-Splines have been used for interpolation. In this paper, a two stage novel and efficient algorithm for suppression of salt and pepper impulse noise for noise levels ranging from 15 % to 95 % using B-splines interpolation is being proposed. The algorithm removes salt and pepper impulse noise from the image in the first stage and in second stage, an edge preserving algorithm has been proposed which regularizes the edges that have been deformed during noise removal process.

A Novel Algorithm for Suppression of Salt and Pepper Impulse Noise in Fingerprint Images Using B-Spline Interpolation

In this paper, we propose a novel method which is an effective implementation of Population Particle Swarm Optimization aiming at optimizing the noise removal process in the case of grayscale images contaminated with salt and pepper noise. A new neighborhood average filter has been used in conjunction with APSO for noise removal. Simulations reveal that the proposed scheme which has been designed specifically for noise removal works well in suppressing noise impulses in images corrupted with different levels of noise. The results of the proposed algorithm are compared with those obtained by PSO-CNN method for gray-scale image noise cancellation.

An Evolutionary Approach to Image Noise Cancellation Using Adaptive Particle Swarm Optimization (APSO)

The accuracy of a proper Biometric Identification and Authentication Systems in Image Forensics depends on the image quality to arrive at a reliable and accuracy result. To get a noise-free fingerprint image, they are applied under the pre-processing and filtering tasks. The Fingerprint Recognition system is often demanded by the accuracy factor. In this paper an attempt is made to evaluate the filtering techniques in the removal of Salt & Pepper Noise. This work proposes a faster and an efficient way to remove salt-and-pepper impulse noise and also the edge-preserving regularization of the henceforth obtained finger print noise free image. In this paper, we propose a two phase mechanism where the noisy pixels are identified and removed in the first phase and only these noisy pixels are involved in cardinal spline edge regularization process in the second phase. Promising results were found even for Noise levels as high as 90% with the proposed algorithm. The results were found to be much better than the previously proposed nonlinear filters or regularization methods both in terms of noise removal as well as edge regularization for image forensics.

A Fast Algorithm for Salt-and-Pepper Noise Removal with Edge Preservation Using Cardinal Spline Interpolation for Intrinsic Finger Print Forensic Images

P. Syamala Jaya Sree

Papers

Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets

A Fast Progressive Image Transmission Algorithm Using Linear Bivariate Splines

A Novel Algorithm for Suppression of Salt and Pepper Impulse Noise in Fingerprint Images Using B-Spline Interpolation

An Evolutionary Approach to Image Noise Cancellation Using Adaptive Particle Swarm Optimization (APSO)

A Fast Algorithm for Salt-and-Pepper Noise Removal with Edge Preservation Using Cardinal Spline Interpolation for Intrinsic Finger Print Forensic Images