845 research outputs found
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
An Adaptive Fuzzy Switching Filter for Images Corrupted by Impulse Noise
In digital images, impulse noise (such as salt and pepper noise) detection and removal is an important process as the images are corrupted by those noise because of transmission and acquisition. The main aim of the noise removal is to suppress the noise when preserving the edge information. The median filter and its derivatives are usually used for this purpose. These filtering techniques usually applied to the overall image and modify the pixel value. The modification in pixel values will be performed in unaffected pixels also. Hence the sufficient removal of impulse using this technique will leads to the reduction in quality of images. In this paper, Adaptive Fuzzy Switching Filter is proposed which is based on fuzzy logic for removing the impulse noise from the affected image. The proposed technique involves three phases. The first phase will detect the impulse noise by considering grayscale distribution among neighboring pixels. In the second phase, grayscale values for the pixels are determined based on the values of neighboring pixels. The final phase implements the fuzzy switching for further improvement in the image preservation. The fuzzy membership function used in the proposed technique is half open fuzzy membership function. The experimental result shows that the proposed adaptive fuzzy switching filter has the better capability of removing the impulse noise from the corrupted image
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
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