A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
PS estimation for image deblurring
CVGIP: Graphical Models and Image Processing
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic selection of edge detector parameters based on spatial and statistical measures
Computer Vision and Image Understanding
Maximum likelihood parametric blur identification based on a continuous spatial domain model
IEEE Transactions on Image Processing
Blur identification by residual spectral matching
IEEE Transactions on Image Processing
A boundary condition based deconvolution framework for image deblurring
Journal of Computational and Applied Mathematics
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Image restoration algorithms often require previous knowledge about the point spread function (PSF) of the disturbance. Deriving the PSF manually from a degraded ideal step-edge in the image is a well known procedure intended mainly for isotropic degradations. A common image degradation that can be approximated as isotropic is the atmospheric blurring in long-distance imaging. This paper proposes an efficient method that automatically finds the best (closest to ideal) step-edge from the degraded image. The identified PSF is then used to restore the image. The existence of a good step-edge in the image may be assumed in cases such as imaging of urban areas, which is common in applications such as visual surveillance and reconnaissance. The criteria employed include the straightness and length of the edge, its strength, and the homogeneity of the step. An efficient algorithm is proposed, and results of automatic blind image restoration based on the automatically extracted PSF are shown.