Peer group image enhancement

Abstract

Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the peer group averaging (PGA) parameters at each point in the image. This local parameter selection allows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms of PGA. Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issues.

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

“…The σ parameter of the kernels was adjusted so that the best PSNR value was achieved. The simulations revealed that independently of the noise contamination level and the applied kernel, for various natural color images, the optimal settings of the block B size r and the α parameter of the ROAD measure are in the range [3][4][5], and the setting r = α = 4 was used for the comparison of the proposed design with competitive denoising methods. Figure 6 depicts the dependence of the best possible PSNR metric on r and α parameters for the test images shown in Fig.…”

“…An efficient family of filters is utilizing the concept of a peer group introduced in [5,11,41,42] and its fuzzy extensions [27,28], in which a combination of impulsive noise detection and a replacement scheme based on averaging is performed. Another group of filters relies on the concept of geodesic digital paths [8,25,26,51], which determines the connection cost between pixels belonging to the processing window used as weights in the averaging process.…”

Adaptive rank weighted switching filter for impulsive noise removal in color images

“…In this way, the new filter is slower than the PGF [23,72] and other designs based on the peer group concept, in which the main computationally demanding step is the calculation of the distances between the central pixel and its neighbors …”

“…In order to alleviate the problems caused by the blurring properties of the VMF and other filters utilizing the ordering scheme, a filtering method using the concept of a peer group was introduced in [71,72] and extensively used in various filtering designs [49,57,66,[73][74][75][76][77][78]. The peer group associated with the central pixel x i of a filtering window W i denotes the set of close pixels, whose distance to x i is not exceeding a given threshold.…”

A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images

“…The Peer Group Averaging (PGA) technique presented in [4]- [7] and extended to the fuzzy context in [8] removes mixed noise by combining a statistical method for impulse noise detection and replacement with an averaging operation between the (fuzzy) peer group members to smooth out Gaussian noise. The difference between these methods relays on how to build the peer groups: [4], [5], [7] use the Fisher Linear Discriminant, [6] uses region analysis…”