Method and apparatus for extended depth-of-field image restoration
First Claim
Patent Images
1. A method of processing a captured image comprising:
- receiving a captured image;
dividing the captured image into a plurality of tiles;
reading at least one prestored eigen-kernel from memory;
reading at least one scalar value from memory; and
for each pixel in the tile;
reading image data in an M×
M window, where M is a number of pixels;
determining a mean of the windowed data;
estimating a variance of noise at a current pixel using the mean and a noise model;
estimating a variance of the image data in the window;
estimating a variance of an original image taking into account degrading blur, noise, and the variance of the image data; and
determining a signal-to-noise ratio at the current pixel, wherein estimating a variance of an original image is performed according to;
σ
x2=(σ
y2−
σ
n2)/φ
(h),wherein φ
(h) is a scalar value representing a strength of the point spread function, σ
n2 is the estimated variance of noise, σ
y2 is the estimated variance of the image data, and σ
x2 is the estimated variance of the original image.
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Abstract
A method and apparatus are disclosed for restoring an image captured through an extended depth-of-field lens. Preprocessed data relating to image degradation is stored and used during an image restoration process.
206 Citations
9 Claims
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1. A method of processing a captured image comprising:
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receiving a captured image; dividing the captured image into a plurality of tiles; reading at least one prestored eigen-kernel from memory; reading at least one scalar value from memory; and for each pixel in the tile; reading image data in an M×
M window, where M is a number of pixels;determining a mean of the windowed data; estimating a variance of noise at a current pixel using the mean and a noise model; estimating a variance of the image data in the window; estimating a variance of an original image taking into account degrading blur, noise, and the variance of the image data; and determining a signal-to-noise ratio at the current pixel, wherein estimating a variance of an original image is performed according to;
σ
x2=(σ
y2−
σ
n2)/φ
(h),wherein φ
(h) is a scalar value representing a strength of the point spread function, σ
n2 is the estimated variance of noise, σ
y2 is the estimated variance of the image data, and σ
x2 is the estimated variance of the original image. - View Dependent Claims (2)
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3. A method of processing a captured image comprising:
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receiving a captured image; dividing the captured image into a plurality of tiles; reading at least one prestored eigen-kernel from memory; reading at least one scalar value from memory; for each pixel in the tile; reading image data in an M×
M window, where M is a number of pixels;determining a mean of the windowed data; estimating a variance of noise at a current pixel using the mean and a noise model; estimating a variance of the image data in the window; estimating a variance of an original image taking into account degrading blur, noise, and the variance of the image data; and determining a signal-to-noise ratio at the current pixel; comparing the determined signal-to-noise ratio with a predetermined threshold; and filtering the captured image signal according to one of; if the determined signal-to-noise ratio is lower than the threshold, then determining reconstruction coefficients by either preserving a value observed or using a smoothing filter; and using the determined signal-to-noise ratio at the current pixel as an input to a polynomial representation to determine reconstruction coefficients to be used for determining a restoration kernel appropriate for the current pixel. - View Dependent Claims (4, 5, 6)
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7. A method of processing a captured image comprising:
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receiving a captured image; dividing the captured image into a plurality of tiles; dividing each of the plurality of tiles into at least one sub-tile; reading at least one scalar value from memory; reading prestored eigen-kernels from memory; determining a signal-to-noise ratio for the current sub-tile; for each sub-tile; using the determined signal-to-noise ratio for the current sub-tile as an input to a polynomial representation to determine reconstruction coefficients to be used for determining a restoration kernel appropriate for the current sub-tile; and using the coefficients to linearly combine the eigen-kernels stored for the current file to determine a restoration kernel that is to be applied at the current pixel; and for each pixel in the tile; reading image data in an M×
M window, where M is a number of pixels;determining a mean of the windowed data; performing one of; estimating a variance of noise at a current pixel or reading an estimated variance of noise for the sub-tile from a memory; determining an estimated variance of the image data in the window; and determining an estimate of a variance of an original image taking into account both degrading blur and noise acting on the original image. - View Dependent Claims (8, 9)
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Specification