3,020 research outputs found
Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal
Most existing image denoising algorithms can only deal with a single type of
noise, which violates the fact that the noisy observed images in practice are
often suffered from more than one type of noise during the process of
acquisition and transmission. In this paper, we propose a new variational
algorithm for mixed Gaussian-impulse noise removal by exploiting image local
consistency and nonlocal consistency simultaneously. Specifically, the local
consistency is measured by a hyper-Laplace prior, enforcing the local
smoothness of images, while the nonlocal consistency is measured by
three-dimensional sparsity of similar blocks, enforcing the nonlocal
self-similarity of natural images. Moreover, a Split-Bregman based technique is
developed to solve the above optimization problem efficiently. Extensive
experiments for mixed Gaussian plus impulse noise show that significant
performance improvements over the current state-of-the-art schemes have been
achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on
Multimedia & Expo (ICME) 201
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
Impulse Noise Removal Using Soft-computing
Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation
GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE
In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute
differences (ROAD) and median of the absolute deviations from the median (MAD) is
proposed. The proposed method consists of three components, including fuzzy noise
detection system, fuzzy switching scheme filtering, and fuzzy parameters
optimization using genetic algorithms (GA) to perform efficient and effective noise
removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a
pixel. Fuzzy inference system is used to justify the degree of which a pixel can be
categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme
that adopts median filter as the main estimator is applied to the filtering. The GA
training aims to find the best parameters for the fuzzy sets in the fuzzy noise
detection.
From the experimental results, the proposed method has successfully removed
mixed impulse noise in low to medium probabilities, while keeping the uncorrupted
pixels less affected by the median filtering. It also surpasses the other methods, either
classical or soft computing-based approaches to impulse noise removal, in MAE and
PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise
well
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