Recent trends in image restoration and enhancement techniques

Author(s):  
A.K. Katsaggelos
Author(s):  
Saurav Prakash

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.


2015 ◽  
pp. 162-177
Author(s):  
Saurav Prakash

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.


2021 ◽  
pp. 13050-13062
Author(s):  
Mrs. Poonam Y. Pawar, Dr. Bharati Sanjay Ainapure

Image Restoration is one of the challenging and essential milestones in the image processing domain. Digital image processing is a technique for manipulating digital images using a variety of computer algorithms. The process of transforming the degraded or damaged image to the original image can be known as Image Restoration. The image restoration process improves image quality by converting the degraded image into the original clean image. The techniques for image restoration are comprised of predefined parameters through which digital image gets processed for refinements. The purpose of restoration is to start with the acquired image and then estimate the original image as accurately as possible. A degraded image can be contaminated by any of a blur or noise or both. Many factors can contribute to image degradation, including poor capture, poor lighting, and poor eyesight. Medical science, defensive sensor systems, forensic detections, and astrology all rely on image restoration for accuracy. This paper discusses various image restoration techniques using recent trends for performance improvements.


1994 ◽  
Vol 05 (01) ◽  
pp. 151-178 ◽  
Author(s):  
B.R. HUNT

Image restoration is the theory and practice of processing an image to correct it for distortions caused by the image formation process. The first efforts in image restoration appeared more than 25 years ago. In this article we review the more recent trends in image restoration and discuss the main directions that are expected to influence the continued evolution of this technology.


Author(s):  
W.A. Carrington ◽  
F.S. Fay ◽  
K.E. Fogarty ◽  
L. Lifshitz

Advances in digital imaging microscopy and in the synthesis of fluorescent dyes allow the determination of 3D distribution of specific proteins, ions, GNA or DNA in single living cells. Effective use of this technology requires a combination of optical and computer hardware and software for image restoration, feature extraction and computer graphics.The digital imaging microscope consists of a conventional epifluorescence microscope with computer controlled focus, excitation and emission wavelength and duration of excitation. Images are recorded with a cooled (-80°C) CCD. 3D images are obtained as a series of optical sections at .25 - .5 μm intervals.A conventional microscope has substantial blurring along its optical axis. Out of focus contributions to a single optical section cause low contrast and flare; details are poorly resolved along the optical axis. We have developed new computer algorithms for reversing these distortions. These image restoration techniques and scanning confocal microscopes yield significantly better images; the results from the two are comparable.


Author(s):  
Richard B. Mott ◽  
John J. Friel ◽  
Charles G. Waldman

X-rays are emitted from a relatively large volume in bulk samples, limiting the smallest features which are visible in X-ray maps. Beam spreading also hampers attempts to make geometric measurements of features based on their boundaries in X-ray maps. This has prompted recent interest in using low voltages, and consequently mapping L or M lines, in order to minimize the blurring of the maps.An alternative strategy draws on the extensive work in image restoration (deblurring) developed in space science and astronomy since the 1960s. A recent example is the restoration of images from the Hubble Space Telescope prior to its new optics. Extensive literature exists on the theory of image restoration. The simplest case and its correspondence with X-ray mapping parameters is shown in Figures 1 and 2.Using pixels much smaller than the X-ray volume, a small object of differing composition from the matrix generates a broad, low response. This shape corresponds to the point spread function (PSF). The observed X-ray map can be modeled as an “ideal” map, with an X-ray volume of zero, convolved with the PSF. Figure 2a shows the 1-dimensional case of a line profile across a thin layer. Figure 2b shows an idealized noise-free profile which is then convolved with the PSF to give the blurred profile of Figure 2c.


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