In [1], we derived an expression for the fundamentallimit to image denoising assuming that the noise-free image is available. In thispaper,we proposeanestimatorforthe boundon the mean squared errorgiven only the noisy image and noise characteristics. To do this, we make use of an assortment

-perceived objects at the database build time. In this paper, we address this outstanding issue by proposing a similarity model for noise-free queries. In our approach, users formulate their queries by specifying objects of interest, and image similarity is based only on these relevant objects. We discuss how our

A task of lossy compression of noisy images providing accessible quality is considered. By accessible quality we mean minimal distortions of a compressed image with respect to the corresponding noise-free image that are observed for the case of optimal operation point (OOP). The ways of reaching

Producing global illumination image without noise is difficult because it requires a large number of samplings. Some approaches, such as photon mapping [Jensen 1996], can render noise-free image, but often cause low frequency biased noise.

Term “super-resolution ” is typically used for a high-resolution image produced from several low-resolution noisy observations. In this paper, we consider the problem of high-quality interpolation of a single noise-free image. Several aspects of the corresponding super-resolution algorithm

Fingerprint recognition is a promising factor for the Biometric Identification and authentication process. The quality of the fingerprint is obtained by the noise free image. To get a noise-free fingerprint image, the preprocessing techniques are applied on image. In this paper, we described

Models of spatial variation in images are central to a large number of low-level computer vision problems including segmentation, registration, and 3D structure detection. Often, images are represented using parametric models to characterize (noise-free) image variation, and, additive noise

increases up to k. To realize it, the median filter using k-nearest noise-free pixels firstly detects noise-free pixels in an image, then re-places each corrupted pixel with the median value of their k-nearest noise-free pixels. The proposed median filter is tested on four real images corrupted by different

Abstract — A novel approach for edge detection in noise-free and noisy images is presented in this paper. The proposed method is based on the number of similar pixels that each pixel in the image may have amongst its neighboring in the filtering window and within a pre-defined intensity range

In this paper, an edge-preserving nonlinear iterative regularization-based image resampling method for a single noise-free image is proposed. Several aspects of the resam-pling algorithm are investigated: choice of discrepancy and regularization norms, improvements of convergence speed using edge