Developments in the theory of image reconstruction and restoration over the past 20 or 30 years are outlined. Particular attention is paid to common estimation structures and to practical problems not properly solved yet. The problem of image reconstruction and restoration is first formulated. Some of the current regularization approaches used to solve the problem are then described. The concepts of a priori information and compound criterion are introduced. A Bayesian interpretation of the regularization techniques is given which clarifies the role of the tuning parameters and indicates how they could be estimated. The practical aspects of computing the solution, first when the hyperparameters are known and second when they must be estimated, are then considered. Conclusions are drawn, and points that still need to be investigated are outlined. >

Image reconstruction and restoration: overview of common estimation structures and problems

The implementation of a truly autonomous expert system for image restoration is discussed. It involves an attempt to combine two unrelated fields, artificial intelligence and image restoration. Another novel aspect of this research is the application of image segmentation to image restoration. The preliminary experimental results are presented. Specific applications include image restoration from multiple versions, restoration from phase information, and restoration from stochastically degraded images. >

/pdf/nonlinear-image-restoration-using-a-segmentation-oriented-3zbwxc47qd.pdf

Nonlinear image restoration using a segmentation-oriented expert system

An image restoration method supported by a rule-based knowledge system that segments the image into a number of homogeneous regions and then applies a different restoration method for each of the regions is presented. It is pointed out that the actual implementation of the system involves two very unrelated fields: artificial intelligence and image restoration. The rule-based expert system solves this conflict by choosing a correct method for each of the different cases. A hierarchical classification of different restoration methods is also included. >

Adaptive image restoration using a rule-based knowledge system

Super-resolution reconstruction (SRR) algorithms are used for getting high-resolution (HR) data from low-resolution
observations. In Maximum a posteriori (MAP) based SRR the observation model is employed for
estimating a HR image that best reproduces the two low-resolution input data sets. The parameters of the
prior play a significant role in the MAP based SRR. This work concentrates on the investigation of the influence
of one such parameter, called temperature, on the reconstructed 3D MR images. The existing approaches on
SRR in 3D MR images use a constant value for this parameter. We use a cooling schedule similar to simulated
annealing for computing the value of the temperature parameter at each iteration of the SRR. We have used
3D MR cardiac data sets in our experiments and have shown that the iterative computation of the temperature
which resembles simulated annealing delivers better results.

Enhancing super-resolution reconstructed image quality in 3D MR images using simulated annealing

The linear image restoration problem is to recover an original brightness distribution X/sup 0/ given the blurred and noisy observations Y=KX/sup 0/+B, where K and B represent the point spread function and measurement error, respectively. This problem is typical of ill-conditioned inverse problems that frequently arise in low-level computer vision. A conventional method to stabilize the problem is to introduce a priori constraints on X/sup 0/ and design a cost functional H(X) over images X, which is a weighted average of the prior constraints (regularization term) and posterior constraints (data term); the reconstruction is then the image X, which minimizes H. A prominent weakness in this approach, especially with quadratic-type stabilizers, is the difficulty in recovering discontinuities. The authors therefore examine prior smoothness constraints of a different form, which permit the recovery of discontinuities without introducing auxiliary variables for marking the location of jumps and suspending the constraints in their vicinity. In this sense, discontinuities are addressed implicitly rather than explicitly. >

Constrained restoration and the recovery of discontinuities

This paper is concerned with developing alagorithms for maximum a posteriori (MAP) restoration of gray level images degraded by multiplicative noise. The MAP algorithm requires the probability density function of the original undegraded image which is rarely available and the probability density function of the corrupting noise. By assuming that the original image is represented by a 2-D noncausal Gaussian Markov random field (GMRF) model, the MAP algorithm is written in terms of GMRF model parameters. The computer implementation of the MAP estimator equations is realized by a stochastic relaxation (SR) algorithm. The SR algorithm generates a sequence of images which converges in probability to the global MAP estimate. Several examples of restoration of the gray level image degraded by multiplicative noise are included.

Stochastic relaxation for MAP restoration of gray level images with multiplicative noise

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

H. Jinchi

Author Tools

Chat about Author

Papers

Stochastic relaxation for MAP restoration of gray level images with multiplicative noise