Results 251 to 260 of about 11,800,567 (295)

Nonlinear Restoration of Noisy Images

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
The restoration of images degraded by an additive white noise is performed by nonlinearly filtering a noisy image. The standard Wiener approach to this problem is modified to take into account the edge information of the image. Various filters of increasing complexity are derived.
Jean-François Abramatic, Leonard M. Silverman   +1 more
openaire   +4 more sources

Image Restoration: Fundamentals of Image Restoration

2014
Abstract Image restoration is the process of recovering an image from a degraded version—usually a blurred and noisy image. Image restoration is a fundamental problem in image processing, and it also provides a testbed for more general inverse problems.
openaire   +1 more source

Contribution to Image and Contours Restoration

Real-Time Imaging, 2001
Summary: Digital images are generally degraded by different sources during their acquisition. This is due of two types of phenomena: the deterministic phenomenon of blur which is introduced by relative motion between a camera and the object, and the stochastic phenomena such as atmospheric turbulence, noise and other factors.
Achour, K., Zenati, N., Laga, H.
openaire   +3 more sources

Synchronous image restoration

1994
We analyse a class of random fields invariant by stochastic synchronous updating of all sites, subject to a generalized reversibility assumption. We give a formal definition and properties of the model, study the problem of posterior simulation, parameter estimation, and then present experimental results in image restoration.
openaire   +2 more sources

Image restoration in radioisotopic imaging systems

Physics in Medicine & Biology, 1967
In radioisotope image visualization, the final image must be interpreted by the human visual system. However, an inherent blurring of the image limits the amount of useful information which can be extracted from it. Attempts have been made to restore the image by compensating for the degradation.
T A, Iinuma, T, Nagai
openaire   +2 more sources

WAVELET-CONSTRAINED IMAGE RESTORATION

International Journal of Wavelets, Multiresolution and Information Processing, 2004
Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations.
Patrick L. Combettes, Jean-Christophe Pesquet   +1 more
openaire   +4 more sources

Limitations of ultrasound imaging and image restoration

Ultrasonics, 1987
The definition of medical ultrasound images is strongly limited by the need for low examination frequencies which is imposed by the high attenuation of acoustic waves in tissues. The filtering effect of imaging systems is described and quantified for echography, transmission tomography and reflection tomography.
A, Herment, J P, Guglielmi, P, Dumee, P, Peronneau, P, Delouche   +4 more
openaire   +2 more sources

Space-Variant Image Restoration

1997
Publisher Summary This chapter offers information on space-variant image restoration. The inhomogeneity assumption introduced in this chapter allows the edges to be directly taken into account by the image model. A stochastic image-generating process is so obtained describing the gray level discontinuities by a space-varying model, where only the ...
De Santis, GERMANI, Alfredo, Jetto L.
openaire   +2 more sources

General restoration filter for vibrated-image restoration

Applied Optics, 1998
Mechanical vibrations are often the principal cause of image degradation. Low temporal-frequency mechanical vibrations involve random image degradation that depends on the instant of exposure. Exact restoration requires the calculation of a specific filter unique to each vibrated image.
A, Stern, N S, Kopeika
openaire   +2 more sources

Image enhancement and restoration

1975
The aim of collecting data is to gain meaningful information about a phenomenon of interest. Unfortunately, often the phenomenon is not a direct physical observable. Instead, e.g., the data at hand may be a linear superposition of the desired quantities.
openaire   +1 more source