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Conclusions
Published in Mario Bertero, Patrizia Boccacci, Christine De Mol, Introduction to Inverse Problems in Imaging, 2021
Mario Bertero, Patrizia Boccacci, Christine De Mol
Step 1 is obvious: it is the basic point for the formulation of the problem of image formation and it can be called the mathematical modeling of the forward problem. In some cases, as in X-ray tomography, this step may be rather easy while it may be very difficult in others (for instance, in the case of emission tomography). In image deconvolution this step is equivalent to the identification of the PSF by means of measurements or to its computation based on a sufficiently accurate model of the imaging system. If one has only a poor knowledge of the PSF one can attempt to improve it by means of methods for blind deconvolution, a term introduced by Stockham, Cannon and Ingebretsen [270] for denoting problems where both the PSF and the object are unknown. One has now at one's disposal many methods of blind deconvolution: spectral methods [270], iterative methods [14] or maximum likelihood methods [186]. The discussion of such methods is beyond the scope of this book.
Comments and concluding remarks
Published in Bertero Mario, Boccacci Patrizia, Introduction to Inverse Problems in Imaging, 2020
Bertero Mario, Boccacci Patrizia
We briefly discuss now the various steps mentioned above. Step 1 is obvious; it is the basic point for the formulation of the problem of image formation. In some cases, as in X-ray tomography, this step may be rather easy while it may be very difficult in others (for instance, in the case of emission tomography). In image deconvolution this step is equivalent to the identification of the PSF by means of measurements or of computations based on a sufficiently accurate model of the imaging system. If one has only a poor knowledge of the PSF one can attempt to improve it by means of methods of blind deconvolution, a term introduced by Stockham, Cannon and Ingebretsen [1] to denote problems where both the PSF and the object are unknown. One has now at one's disposal many methods of blind-deconvolution: spectral methods [1], iterative methods [2] or maximum likelihood methods [3].
Light Microscopes
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
In Chapter 4, we discussed that Gaussian smoothing is used to reduce noise in an image. The noise reduction is achieved by smearing the pixel value at one location to all its neighbors. Any optical system performs a similar operation with a kernel called a Point Spread Function (PSF). It is the response of an optical system to a point input or point object as a consequence of diffraction. When a point source of light is passed through a pinhole aperture, the resultant image on a focal plane is not a point, but instead the intensity is spread to multiple neighboring pixels. In other words, the point image is blurred by the PSF. The PSF is dependent on the numerical aperture of the lens. A lens with a high numerical aperture produces a PSF of smaller width.
Neutron Pinhole Characterization and Analysis for Three-Dimensional As-Built Model Reconstruction
Published in Fusion Science and Technology, 2023
Nikolaus Christiansen, Derek Schmidt, John Martinez, Valerie Fatherley, Justin Jorgenson, Noah Birge, Verena Geppert-Kleinrath, Carl Wilde
The model and axis files are critical for neutron image reconstruction, as the files define the relevant aperture geometry. The image reconstruction toolkit uses this geometry to calculate neutron and/or gamma transmission through the PHA to the detector. The function that defines the transmission from a point in the source plane to a point in the image plane is referred to as the point-spread function (PSF). The source distribution is determined iteratively by using these PSFs to project an estimated source distribution to the detector. This simulated image is then compared to the recorded image, and the estimated source distribution is updated according to an expectation maximization algorithm.[1] Hence, an accurate PHA description, which is provided by the model and axis files, is imperative for precise image reconstructions.
ASALD: adaptive sparse augmented lagrangian deblurring of underwater images with optical priori
Published in The Imaging Science Journal, 2022
Chrispin Jiji, R. Nagaraj, Vivek Maikandavel
It can generate less time and cheap performance in a reliable way than other applications. It also includes built-in lens, prism parts, expert on new lenses, user-accessible, fastest optimization, the discovery of patent lenses, manufacturing of supply structures, etc. Significant optimization structures were compliant with RMS, PSF, MTF, simple operator description of accurate parameters, quick, accurate optimization, specific eminence, etc. CodeV also offers diffraction analysis software for the accurate evaluation of the performance. PSF defines an object or point source as the imaging scheme's optical response. It is a significant unit that plays a major role in image production. The diffraction form of 3D illumination, produced from a point source and diffused onto the plane of the image. For 2D light distribution, PSF is used to point out the lens difficulties and to examine out-of-focus information for image formation, as seen in Figure 3. The causes of deterioration are turbidity, floating materials, underwater light absorption, and the combination of a function called PSF. We can restore the underwater images with UWPSF estimates [26,27].
Improving reconstruction of targets hidden in scattering media by introducing the Lucy-Richardson deconvolution algorithm into a system of multiview optical projections
Published in Journal of Modern Optics, 2022
Ariela Tsabary, David Abookasis
In our scheme, objects with different shapes and sizes were hidden separately in a variety of turbid media which was illuminated by different polarization conditions. As a result, multiple polarized speckled sub-images from different views (projections) were recorded. Next, the above procedure was performed under the same conditions but only the medium, without the object, was illuminated by light point source to obtain the system PSF. Knowledge of the system PSF is essential to the deconvolution process. In offline image analysis, each sub-image of the speckled object was deconvolved with LRA with the system PSF image of the speckled point-like source. Following processing, only those sub-deconvolved images with contrast value above defined threshold parameter are selected. Finally, the selected ‘best’ images are shifted to a common center and then superimposed together to form a single, average image. Thus, the shape of the hidden object is revealed with reduced scattering noise, enabling delineation of the hidden target shape. The main contribution of this work is the application of LRA on each sub-projection and the sorting of individual sub-deconvolved images to the imaging of objects hidden in media of varying compositions. The results presented in this paper support the efficacy of the proposed approach.