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US 8,131,097 B2Granted
Method and apparatus for extended depth-of-field image restoration
AssigneeAptina Imaging Corporation
InventorsDan Lelescu, Robert Mullis, Pravin Rao, Kartik Venkataraman, Cheng Lu, Junqing Chen
Life of this patent
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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.
Claims9
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:
- σx2=(σy2−σn2)/φ(h),
- wherein φ(h) is a scalar value representing a strength of the point spread function, σn2 is the estimated variance of noise, σy2 is the estimated variance of the image data, and σx2 is the estimated variance of the original image.
12 more elements
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;
- 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.
15 more elements
A method of processing a captured image comprising:
- 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.
16 more elements
Dependencies are inferred from the claim text.