Several adaptive order statistic filters (OSF) are developed and compared for channel characterization and noise suppression in images and 3-D CT data. Emphasis has been put on the situation when a noisefree reference image is not available but instead we can have a sequence of two noisy versions of the same image (or a 3-D data slice). One of the noisy images is used as the reference in the OS...

Several adaptive order statistic filters (OSF) are developed and compared for channel characterization and noise suppression in images and 3-D CT data. Emphasis has been put on the situation when a noise-free reference image is not available but instead we can have a sequence of two noisy versions of the same image (or a 3-D data slice). One of the noisy images is used as the reference in the O...

Group sparsity or nonlocal image representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image, that is, the similar patches collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. In this paper we propose a new prior model for imag...

Each row shows few spatial frames and the image time profile. We observe a similar behavior as seen in fig.1. k-t FOCUSS showed some temporal blur (see yellow arrow in (b)). BCS had better temporal fidelity but suffered from noisy artifacts (see arrows in (c) due to learning noisy patterns. Sparse BCS resulted in reconstructions with reduced noise like artifacts without compromising on the spat...

The requirement for image denoising is encountered in many practical applications. Such as, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images analyzed in a noisy environment or internal noise in communication channels. In this review paper image denoising is studied along with the common source of noise and quality measures. After rev...

State-of-the-art image denoising algorithms attempt to recover natural image signals from their noisy observations, such that the statistics of the denoised image follow the statistical regularities of natural images. One aspect generally missing in these approaches is that the properties of the residual image (defined as the difference between the noisy observation and the denoised image) have...

Labels associated with social images are valuable source of information for tasks of image annotation, understanding and retrieval. These labels are often found to be noisy, mainly due to the collaborative tagging activities of users. Existing methods on annotation have been developed and verified on noise free labels of images. In this paper, we propose a novel and generic framework that explo...

Both regularization and compression are important issues in image processing and have been widely approached in the literature. The usual procedure to obtain the compression of an image given through a noisy blur requires two steps: first a deblurring step of the image and then a factorization step of the regularized image to get an approximation in terms of low rank nonnegative factors. We exa...

In image processing, the most significant challenges have been addressed due to the image denoising. Since, the segmentation of the original image components from the noisy image has high complexity. Therefore, the different image segmentation techniques were developed for effective segmentation by categorizing the noisy images. Among different methods, the image denoising including with the ed...

In this paper, classified and comparative study of edge detection algorithms are presented. Experimental results prove that Boie-Cox, ShenCastan and Canny operators are better than Laplacian of Gaussian (LOG), while LOG is better than Prewitt and Sobel in case of noisy image. Subjective and objective methods are used to evaluate the different edge operators. The morphological filter is more imp...