Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

The Bitonic Filter: Linear Filtering in an Edge-preserving Morphological Framework.

A new filter is presented which has better edge and detail preserving properties than a median, noise reduction capability similar to a Gaussian, and is applicable to many signal and noise types. It is built on a definition of signal as bitonic, i.e. containing only one local maxima or minima within the filter range. This definition is based on data ranking rather than value, hence the bitonic filter comprises a combination of non-linear morphological and linear operators. It has no data-level-sensitive parameters and can locally adapt to the signal and noise levels in an image, precisely preserving both smooth and discontinuous signals of any level when there is no noise, but also reducing noise in other areas without creating additional artefactual noise. Both the basis and the performance of the filter are examined in detail, and it is shown to be a significant improvement on the Gaussian and median. It is also compared over various noisy images to the image-guided filter, anisotropic diffusion, non-local means, the grain filter, and self-dual forms of levelling and rank filters. In terms of signal-to-noise, the bitonic filter outperforms all these except non-local means, and sometimes anisotropic diffusion. However it gives good visual results in all circumstances, with characteristics which make it appropriate particularly for signals or images with varying noise, or features at varying levels. The bitonic has very few parameters, does not require optimisation nor prior knowledge of noise levels, does not have any problems with stability, and is reasonably fast to implement. Despite its non-linearity, it hence represents a very practical operation with general applicability

Study Of Gaussian & Impulsive Noise Suppression Schemes In Images

Noise is introduced into images usually while transferring and acquiring them.The main type of noise added while image acquisition is called Gaussian noise while Impulsive noise is generally introduced while transmitting image data over an unsecure communication channel , while it can also be added by acquiring. Gaussian noise is a set of values taken from a zero mean Gaussian distribution which are added to each pixel value. Impulsive noise involves changing a part of the pixel values with random ones. Various techniques are employed for the removal of these types of noise based on the properties of their respective noise models. Impulse Noise removal algorithms popularly use ordered statistics based ¯lters. The ¯rst one is an adaptive ¯lter using center-weighted median. In this method, the di®erence of the center weighted mean of a neighborhood with the central pixel under consideration is compared with a set of thresholds. Another method which takes into account the presence of the noise free pixels has been implemented.It convolutes the median of each neighborhood with a set of convolution kernels which are oriented according to all possible con¯gurations of edges that contain the central pixel,if it lies on an edge. A third method which deals with the detection of noisy pixels on the binary slices of an image is implemented. It is based on threshold Boolean ¯ltering. The ¯lter inverts the value of the central pixel if the number of pixels with values opposite to it is more than the threshold. The fourth method has an e±cient double derivative detector, which gives a de- cision based on the value of the double derivative. The substitution is done with the average gray scale value of the neighborhood. Gaussian Noise removal algorithms ideally should smooth the distinct parts of the image without blurring the edges.A universal noise removing scheme is implemented which weighs each pixel with respect to its neighborhood and deals with Gaussian and impulse noise pixels di®erently based on parameter values for spatial, radiometric and impulsive weight of the central pixel. The aforementioned techniques are implemented and their results are compared subjectively as well as objectively

Soft morphological filter optimization using a genetic algorithm for noise elimination

Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well

Soft morphological filter optimization using a genetic algorithm for noise elimination

Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well