Nikon Corporation

Radka Tezaur

Gazi Ali

Li Hong

Mark K Takita

Tetsuji Kamata

Scott Coakley

Deblurring a blurry image () includes the steps of (i) computing a spatial mask (); (ii) computing a modified blurry image () using the blurry image () and the spatial mask (); and (iii) computing a latent sharp image () using the modified blurry image () and a point spread function (). Additionally, the image () can be analyzed to identify areas of the image () that are suitable for point spread function estimation. Moreover, a region point spread function () can be analyzed to classify the point spread function(s) as representing (i) motion blur, (ii) defocus blur, or (iii) mixed motion blur and defocus blur. A point spread function () can also be estimated.

Algorithm and device for image processing

A method for deblurring a blurry image () includes the steps of: performing a first phase of deconvolution () with a first phase regularization spatial mask () to reconstruct the main edges and generate a first phase latent sharp image () having reconstructed main edges; and performing a second phase of deconvolution () with a second phase regularization spatial mask () to reconstruct the texture and generate a second phase latent sharp image (). The second phase regularization spatial mask () can be different from the first phase regularization spatial mask ().

Multiple phase method for image deconvolution

A method for estimating an optics point spread function () of a lens assembly () includes (i) providing a test chart (); (ii) providing a capturing system (); (iii) capturing a test chart image () of the test chart () with the capturing system () using the lens assembly () to focus light onto the capturing system (); (iv) selecting an image area () from the test chart image (); (v) dividing the image area () into a plurality of area blocks (); and (vii) estimating an area point spread function () of the image area () using a PSF cost function that sums the fidelity terms of at least two of the area blocks ().

Point spread function estimation of optics blur

Deblurring a blurry image () includes the steps of (i) computing a spatial mask (); (ii) computing a modified blurry image () using the blurry image () and the spatial mask (); and (iii) computing a latent sharp image () using the modified blurry image () and a point spread function (). Additionally, the image () to can be analyzed to identify areas of the image () that are suitable for point spread function estimation. Moreover, a region point spread function () can be analyzed to classify the point spread function(s) as representing (i) motion blur, (ii) defocus blur, or (iii) mixed motion blur and defocus blur. A point spread function () can also be estimated.

A group point spread function () for a blurry image () can be determined by dividing the blurry image () into a plurality of image regions (), estimating a region point spread function () for at least two of the image regions (); and utilizing at least two of the region point spread functions () to determine the group point spread function (). The group point spread function () can be determined by the decomposition of the estimated region point spread functions () into some basis function, and subsequently determining a representative coefficient from the basis functions of the region point spread functions () to generate the group point spread function ().

Globally dominant point spread function estimation

A method for classifying a test image () includes the steps of building a classifier (), and classifying the test image () with the classifier (). After the test image () is classified, the test image () can be subsequently processed (e.g. deblurred) with improved accuracy. The classifier () can classify and distinguish between PSF features associated with motion blurred images, and PSF features associate with defocus blurred images. The classifier () can be built using a plurality of training images (), and extracting one or more training features from each the training images (). The PSF features can include image moments of the point spread function, and/or geometric features of the point spread function.

Point spread function classification using structural properties

A method for deblurring a blurry image () includes utilizing a spatial mask and a variable splitting technique in the latent sharp image estimation cost function. Additionally or alternatively, the method can include the utilizing a spatial mask and a variable splitting technique in the PSF estimation cost function. The spatial mask can be in a regularization term of either or both the latent sharp image estimation cost function and the PSF cost function. The latent sharp image estimation cost function can be used for non-blind deconvolution. Alternatively, one or both cost functions can be used for blind deconvolution.

Algorithm for minimizing latent sharp image cost function and point spread function cost function with a spatial mask in a regularization term

A method for deblurring a blurry image () includes utilizing a spatial mask and a variable splitting technique in the latent sharp image estimation cost function. Additionally or alternatively, the method can include the utilizing a spatial mask and a variable splitting technique in the PSF estimation cost function. The spatial mask can be in a fidelity term in either or both the latent sharp image estimation cost function and the PSF cost function. The latent sharp image estimation cost function can be used for non-blind deconvolution. Alternatively, one or both cost functions can be used for blind deconvolution.

Algorithm for minimizing latent sharp image cost function and point spread function with a spatial mask in a fidelity term

A method for detecting a dust spot () in a digital image () includes the steps of determining () an expected dust spot configuration () of the dust spot () in the image (); applying () a statistic order filter to a value channel of an HSV color space of the image to generate a filtered value for each pixel being evaluated (), the filtered value being based upon the expected dust spot configuration (); and comparing () the filtered value to an actual color space value of a plurality of pixels () in the digital image () to generate () a binary image (). In one embodiment, the method can also include the step of comparing () the binary image () to the expected dust spot configuration () to determine a probability of the presence of the dust spot () in the digital image ().

Method for detecting existence of dust spots in digital images based on locally adaptive thresholding

A PSF cost function used for determining a PSF kernel () of at least a portion of an image () includes (i) a first fidelity term having a first direction derivative, (ii) a first fidelity term weight for the first fidelity term, (iii) a second fidelity term having a second direction derivative, and (iv) a second fidelity term weight for the second fidelity term. Another PSF cost function for determining a PSF kernel () includes (i) a first fidelity term with a first derivative in a dominant edge direction (d), and (ii) a second fidelity term having a second derivative in a perpendicular direction(p) to the dominant edge direction.

Point spread function cost function with non-uniform weights

A system for estimating an illuminant of a scene that was captured by an input image includes a control system that generates an input gamut that includes the input colors for the input image. Further, the control system compares the input gamut to an illuminant database that includes separate information from a gamut of observable colors for a plurality of possible illuminants to estimate the possible illuminant. Each illuminant can be represented by a separate illuminant gamut. Moreover, each gamut can be organized as a matrix. Additionally, the control system can combine the input gamut with each of the illuminant gamuts to generate a separate union gamut for each of the possible illuminants. The control system can compare the union gamut and the corresponding illuminant gamut for each possible illuminant to estimate the illuminant. After estimating the possible illuminant, the control system performs color correction on the input image.

Automatic illuminant estimation and white balance adjustment based on color gamut unions

An exemplary stage apparatus has a motor, stage, and position-measuring device. The motor has a planar stator and moving-coil mover (planar motor). The stator is a checkerboard magnet array extending in an x-y plane and producing a magnetic field having a field period of 2&#x3c0; in a u-v coordinate system rotated 45&#xb0; from the x-y coordinate system of the plane. The stage, coupled to the mover, moves with corresponding motions of the mover relative to the stator. The position-measurement device includes a first group of four magnetic-field sensors that are movable with the stage. The sensors are situated at integer multiples of &#x3c0;/2 from each other in u- and v-directions of the u-v coordinate system. The sensors produce respective data regarding a respective component of the magnetic field at, and hence the position of, the respective sensor within the period of the magnetic field.

Apparatus and methods for determining an initially unknown commutation position of a member moved by a planar motor

A method for sharpening a captured image includes the steps of (i) identifying a plurality of edge pixels in the captured image, (ii) reviewing the plurality of edge pixels to identify one or more line pixels, and one or more non-line pixels in the captured image (), and (iii) sharpening the captured image utilizing a first level of overshoot control for the non-line pixels (), and utilizing a second level of overshoot control for the line pixels () The method also includes the steps of (i) identifying an intensity value for each of a plurality of neighboring pixels that are positioned near a selected pixel in a predetermined pixel window that have the highest intensity values and the lowest intensity values.

Adaptive overshoot control for image sharpening

A method for reducing blurring in a blurred image () of a scene () includes the steps of: (i) creating an edge mask () from the blurred image (); (ii) extending the edge mask (; (iii) forming an initial array (e.g. extending the blurred image (); and (iv) performing Lucy-Richardson iterations, with masking. With the deblurring method disclosed herein, the reconstructed adjusted image () (i) does not have (or has significantly less) ringing artifacts around the edges () of the captured object(s) (C), and (ii) does not have (or has significantly less) ringing artifacts around border (). As a result thereof, the adjusted image () is more attractive and more accurately represents the scene ().

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Belmont, CA, United States of America

Radka Tezaur