Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The nonextensive entropy is a recent development in statistical mechanics and it is a new formalism in which a real quantity q was introduced as parameter for physical systems that present long range inte...

ABSTACT An image is often corrupted by noise in its acquisition and transmission by various kinds of noises. Image denoising using thresholding methods means find appropriate value (threshold)which separates noise values to actual image values without affecting the significant features of the image.Wavelet transform represents image energy in compact form and representation helps in determining...

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. T...

8 In this paper a method for detection of image forgery in lossy compressed digital images known as error level analysis (ELA) is presented and it’s noisy components are filtered with automatic wavelet soft-thresholding. With ELA, a lossy compressed image is recompressed at a known error rate and the absolute differences between these images, known as error levels, are computed. This method mig...

Image denoising is an important pre-processing step in most imaging applications. Block Matching and 3D Filtering (BM3D) is considered to be the current state-of-art algorithm for additive image denoising. But this algorithm uses a fixed hard thresholding scheme to attenuate noise from a 3D block. Experiments show that this fixed hard thresholding deteriorates the performance of BM3D because it...

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing Even though much work has been done in the field of wavelet thresholding, most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for suppression of noise in image by fusing the wavelet Denoising technique ...

A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get image of layers. In this paper, an automatic method based on FCM clustering and Otsu thresholding is introduced in order to extract quantitative information from FMI images. All pixels are clustered using FCM clustering algorithm at the f irst step. The second step uses KNN for other clustering....

Multilevel image thresholding is an important operation in many analyses which is used in many applications. Selecting correct thresholds is a critical issue. In this paper, Bacterial Foraging (BF) algorithm based on Tsallis objective function is presented for multilevel thresholding in image segmentation. Experiments to verify the efficiency of the proposed method and comparison to Genetic Alg...

Medical image segmentation is a method of extracting the desired parts and features from the input medical image data. The conventional FCM algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is extremely susceptible to noise since it uses intensity values for clustering the image. This paper aims to develop 3-class FCM algorithm with thresholding ...

The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as ...