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

Review on Colour Image Denoising using Wavelet Soft Thresholding Technique

In this modern age of communication the image and video is important as Visual information transmitted in the form of digital images, but after the transmission image is often ruined with noise. Therefore the received image needs to be processing before it can be used for further applications. Image denoising implicates the manipulation of the image data to produce a high quality of image without any noise. Most of the work which had done in color scale image is by filter domain approach, but we think that the transform domain approach give great result in the field of color image denoising.. This paper reviews the several types of noise which corrupted the color image and also the existing denoising algorithms based on wavelet threshodling technique. DOI: 10.17762/ijritcc2321-8169.15039

Improved texture image classification through the use of a corrosion-inspired cellular automaton

In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from corrosion modeling and cellular automata to generate a texture descriptor. The core processes of metal (pitting) corrosion are identified and applied to texture images by incorporating the basic mechanisms of corrosion in the transition function of the cellular automaton. The surface morphology of the image is analyzed before and during the application of the transition function of the cellular automaton. In each iteration the cumulative mass of corroded product is obtained to construct each of the attributes of the texture descriptor. In a final step, this texture descriptor is used for image classification by applying Linear Discriminant Analysis. The method was tested on the well-known Brodatz and Vistex databases. In addition, in order to verify the robustness of the method, its invariance to noise and rotation were tested. To that end, different variants of the original two databases were obtained through addition of noise to and rotation of the images. The results showed that the method is effective for texture classification according to the high success rates obtained in all cases. This indicates the potential of employing methods inspired on natural phenomena in other fields.Comment: 13 pages, 14 figure

An Efficient Threshold Based Mixed Noise Removal Technique

Removing or reducing noises from image is very important task in image processing. This paper presents an efficient noise removal technique to restore original digital images corrupted by mixed noise. The proposed filtering technique consists of three steps: noisy pixel detection using fuzzy flag, mixed noise filtering step and calculating threshold value remove the pixel value with replacement conditions. Noises in this methodology are the combination of gaussian noise and salt and pepper noise. This methodology reduces the mixed noise without lossing edges sharpness and information. This methodology gives better results existing many fuzzy algorithms. The proposed technique shows better peak signal noise ratio result with thresholding replacement conditions. Hence, this mixed noise removal technique finds application in numerous segments of image process like digital tv, medical image process, camera, police work systems etc. Wiener filter is used for image enhancement

Composite median wiener filter based technique for image enhancement

Image processing begins with image enhancement to improve the quality of the information existing in images for further processing. Noise is any unwanted object that affects the quality of original images. This always happened during the acquisition of images, which cause gaussian noise via photoelectric sensor. Also, impulse noise as well is introduced during transferring of some images from one place to another because of unstable network. Hence, these noises combine to form mixed noise in some images, which change the form and loss of information in the images. Filtering techniques are usually used in smoothing and sharpness of images, extraction the useful information and prepare an image for analysis processing. In this research, a novel technique of hybrid filter for enhancing images degraded by mixed noise has been exhibited. The proposed model of the novel filter uses the concept of two element composite filter. This technique improved the fusion of Median filter and Wiener filter to eliminate mixed form of noise from digital image created during image acquisition process. Composite Median Wiener(CMW) is not two filters in series, yet it can remove the blurredness, keep the image edges, and eliminate the mixed noise from the image. The result of CMW filter application on noisy image shows that it is an effective filter in enhancing the quality image