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Space-Variant Image Restoration
1997Publisher 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.
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A vector quantizer for image restoration
Proceedings of 3rd IEEE International Conference on Image Processing, 1998This paper presents a novel technique for image restoration based on nonlinear interpolative vector quantization (NLIVQ). The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. It is trained on image pairs consisting of an original image and its diffraction-limited counterpart.
Mariappan S. Nadar +4 more
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Image enhancement and restoration
1975The 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.
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2018
Image Denoising: Past, Present, and Future, X. Li Historical Review of Image Denoising First Episode: Local Wiener Filtering Second Episode: Understanding Transient Events Third Generation: Understanding Nonlocal Similarity Conclusions and Perspectives Fundamentals of Image Restoration, B.K.
Xin Li, Bahadir K. Gunturk
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Image Denoising: Past, Present, and Future, X. Li Historical Review of Image Denoising First Episode: Local Wiener Filtering Second Episode: Understanding Transient Events Third Generation: Understanding Nonlocal Similarity Conclusions and Perspectives Fundamentals of Image Restoration, B.K.
Xin Li, Bahadir K. Gunturk
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Quantitative image restoration
SPIE Proceedings, 2010Even with the most extensive precautions and careful planning, space based imagers will inevitably experience problems resulting in partial data corruption and possible loss. Such a loss occurs, for example, when individual image detectors are damaged. For a scanning imager this results in missing lines in the image.
Michael Grossberg +2 more
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Image Gathering and Restoration [PDF]
, 1997 This chapter focuses the mathematical development begun in Chapter 2 on digital image restoration. First, Section 3.1 develops the unconstrained Wiener filter that restores the image with an interpolation lattice that is sufficiently dense to completely suppress the blurring and raster effects of the image-display process.
Friedrich O. Huck +2 more
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Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003
A new iterative processing for image restoration is proposed based on the turbo iterative principle. For this purpose, two uncorrelated component images issued from an original image are differently filtered and the pieces of information extracted from both filters are mutually exchanged in the iterative process.
Bao Guan, Hong Sun, Henri Maitre
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A new iterative processing for image restoration is proposed based on the turbo iterative principle. For this purpose, two uncorrelated component images issued from an original image are differently filtered and the pieces of information extracted from both filters are mutually exchanged in the iterative process.
Bao Guan, Hong Sun, Henri Maitre
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SPIE Proceedings, 1985
The presence of signal-dependent image noise sources commonly leads to the use of nonlinear restoration techniques. This paper reviews the use of signal-dependent noise models such as film grain noise and photoelectronic shot noise and describes some of the nonlinear estimators which have been developed for these situations.
Rangachar Kasturi, John F. Walkup
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The presence of signal-dependent image noise sources commonly leads to the use of nonlinear restoration techniques. This paper reviews the use of signal-dependent noise models such as film grain noise and photoelectronic shot noise and describes some of the nonlinear estimators which have been developed for these situations.
Rangachar Kasturi, John F. Walkup
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Image restoration and enhancement
Physics in Technology, 1979Report on Image restoration and enhancement, Institution of Electrical Engineers, 19 December 1978 at the IEE, Savoy Place.
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Image Evaluation and Restoration*†
Journal of the Optical Society of America, 1966The evaluation of an image must depend upon the purpose for which the image was obtained and the manner in which the image is to be examined. Where the goal is extraction of information and where the image is to be processed prior to viewing, the information content of the image is the only true evaluation criterion.
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