An image-capturing unit generates an image signal corresponding to an image of a subject formed by a photographing optical system. A point spread function generating unit generates varying point spread functions corresponding to various distances based upon two-dimensional distance distribution information indicating the two-dimensional distance distribution of the subject detected by a distance detection unit and an image blur signal indicating an image blur occurring during the exposure period which is detected by a blur detection unit. An image repair unit divides the image signal into partial images based upon the distance distribution information, executes image processing to repair the blur in the individual partial images by using the point spread functions corresponding to the various distances and creates a whole image by synthesizing the partial images in which the blur has been repaired.

Image creating device and image creating method

Multiple blur of star image and the restoration under dynamic conditions

Measurement of sinusoidal vibration from motion blurred images

A signal processing algorithm has been developed in which a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of any deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated Self-deconvolving Data Reconstruction Algorithm (SeDDaRA), has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is non-iterative, computationally efficient, and requires little user input. Implementation is straight-forward, allowing inclusion into all types of signal processing software and hardware. The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method is by educated guess where the value is deemed a constant of frequency that ranges between zero and one. This approach requires no knowledge of the original data or the degradation and is quite effective. The second method compares the frequency spectrum of the degraded data to the spectrum of a signal with the desired frequency response. This approach produces a superior result, but requires additional processing.

Signal processing using the self-deconvolving data reconstruction algorithm

A method of numerically calculating the optical transfer function appropriate to any type of image motion and vibration, including random, has been recently developed. This method has been verified experimentally with real vibrations, and the close agreement justifies implementation in image restoration from blur deriving from any type of image motion, including random displacement. Here, image restorations of actual physically degraded images are presented, based on a constrained least squares improvement of the original Wiener filter. Even for extreme vibrations where the blur extent is much larger than the blurred detail, restoration is quite complete. The key to restoration is the determination of the optical transfer function unique to the particular image motion and vibration causing the blur. Results are also presented for low vibration frequency motion of random blur extent, as well as for high vibration frequencies.

Restoration of images degraded by extreme mechanical vibrations

A method of calculating numerically the optical transfer function appropriate to any type of image motion and vibration, including random ones, has been developed. This method has been verified experimentally, and the close agreement justifies implementation in image restoration for blurring deriving from any type of image motion. The goal of this research is to recover the original image from its degraded version. There are many methods of image restoration based on the point spread function. One of the common methods is the Wiener filter. Here, some image restorations of physically degraded images are presented, based on a constrained least squares improvement of the original Wiener filter. The key to restoration is determination of the optical transfer function unique to each particular image motion and vibration.

Restoration of images degraded by mechanical vibrations

http://ieeexplore.ieee.org/iel2/4429/12532/00577139.pdf

A. Cotter

Papers

Restoration of images degraded by extreme mechanical vibrations

Restoration of images degraded by mechanical vibrations

Restoration of images degraded by mechanical vibrations