Abstract: Fractal decompression and the Retinex algorithm are combined to produce a color constancy method. By using this approach, color constancy and image compression can be achieved simultaneously.

Abstract: The color calibration using colored rays method achieves illuminant independence in calibrating digital still cameras. A constraint is developed using matrix-vector operations and properties of the Kronecker product. The constraint ensures similar calibration performance between colored rays set and the Macbeth ColorChecker. An optimization scheme using orthogonal non-negative matrix factorization with the new constraint is able to obtain the optimal colored rays set. Then, by acquiring an image of the optimal colored rays set, a camera is able to determine an adjustment matrix for color calibration. Experimental results show that compared to traditional calibration approach for digital still cameras, the colored rays approach gives smaller color error under various evaluation illuminants with only one shot needed.

Abstract: A system and method for denoising using signal dependent adaptive weights includes an imaging device that captures image data corresponding to a photographic target. A denoising manager identifies similar pixels from said image data that are located within a pre-defined processing window around the pixel to be denoised. The denoising manager computes signal-dependent weighting values that correspond to respective ones of the similar pixels. The denoising manager then calculates the denoised pixel value by utilizing the weighting values in conjunction with raw pixel values of the similar pixel set. In this manner all pixels in the image are denoised.

Abstract: A spatial transformation methodology provides a new image interpolation scheme, or analyzes an already existing one. Examples of spatial operations include but are not limited to, demosaicing, edge enhancement or sharpening, linear filtering, and non-linear filtering. A demosaicing operation is described herein, although the scheme is applied generally to spatial transformation operations. The spatial transformation methodology includes detailed expressions for the noise covariance after a spatial operation is performed for each of the three color channels, red, green, and blue. A color filter array is in the form of a Bayer pattern and demosaicing is performed using a 4-neighbor bilinear interpolation. Using lattice theory, the spatial transformation methodology predicts noise covariance after demosaicing in terms of the input noise covariance and an autocorrelation function of the image is determined for a given selectable number of shifts.

Abstract: A method and system are provided for approximating spectral sensitivities of a particular image sensor, the image sensor having a color filter array positioned over the image sensor. In one example of the method, the method involves measuring spectral sensitivities of a set of image sensors each having a color filter array positioned over the image sensor, calculating mean spectral sensitivities of the set of image sensors for each color within the color filter array, measuring outputs of a particular image sensor when capturing a picture of a plurality of color patches under a first illuminant and calculating spectral sensitivities of the particular image sensor using the mean spectral sensitivities and the output of the particular image sensor. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to determine outputs of the particular image sensor under a second illuminant.

Abstract: A color chart for color calibration of imaging devices that has nearly identical calibration performance as the Macbeth ColorChecker or another set of reference colors, but with substantially fewer color patches. For example, the color chart has similar 2nd order statistical characteristics, auto-correlation matrix and major principal components as the Macbeth ColorChecker. The color chart is developed by applying Orthogonal Non-negative Matrix Factorization (ONMF) to the set of reference colors, using non-negativity and smoothness constraints to achieve physically realizable colors and using orthogonality constraints to obtain similar statistical properties to that of any input set of reflectances including, but not limited to, the Macbeth ColorChecker. Seven colors provide nearly identical calibration performance to that of twenty-four colors in the Macbeth ColorChecker.

Abstract: An image denoising system and method of implementing the image denoising system is described herein. Noise is decomposed within each channel into frequency bands, and sub-band noise is propagated. Denoising is then able to occur at any node in a camera pipeline after accurately predicting noise that is signal level-dependent, frequency dependent and has inter-channel correlation. A methodology is included for estimating image noise in each color channel at a sensor output based on average image level and camera noise parameters. A scheme is implemented for detecting a peak-white image level for each color channel and predicting image level values for representative colors. Based on a noise model and camera parameters, noise levels are predicted for each color channel for each color patch and these noise levels are propagated to the denoising node. A three dimentional LUT correlates signal level to noise level. Then, a denoising threshold is adaptively controlled.

Abstract: A fast accurate multi-channel frequency dependent scheme for analyzing noise in a signal processing system is described herein. Noise is decomposed within each channel into frequency bands and sub-band noise is propagated. To avoid the computational complexity of a convolution, traditional methods either assume the noise to be white, at any point in the signal processing pipeline, or they just ignore spatial operations. By assuming the noise to be white within each frequency band, it is possible to propagate any type of noise (white, colored, Gaussian, non-Gaussian and others) across a spatial transformation in a very fast and accurate manner. To demonstrate the efficacy of this technique, noise propagation is considered across various spatial operations in an image processing pipeline. Furthermore, the computational complexity is a very small fraction of the computational cost of propagating an image through a signal processing system.

Abstract: A spatial transformation methodology provides a new image interpolation scheme, or analyzes an already existing one. Examples of spatial operations include but are not limited to, demosaicing, edge enhancement or sharpening, linear filtering, and non-linear filtering. A demosaicing operation is described herein, although the scheme is applied generally to spatial transformation operations. The spatial transformation methodology includes detailed expressions for the noise covariance after a spatial operation is performed for each of the three color channels, red, green, and blue. A color filter array is in the form of a Bayer pattern and demosaicing is performed using a 4-neighbor bilinear interpolation. Using lattice theory, the spatial transformation methodology predicts noise covariance after demosaicing in terms of the input noise covariance and an autocorrelation function of the image is determined for a given selectable number of shifts.

Abstract: A color determination method utilizes color matching functions to approximate the imaging system's sensitivity characteristics. The illuminant conditions are modeled according to known illuminant intensity versus wavelength functions. Non-negative Matrix Factorization (NMF) is applied to a set of known reflectance data to decompose the known reflectance data set into a defined number of NMF basis vectors. In general, for an N-color based imaging system, N NMF basis functions are determined. Since basis functions provided by NMF are non-negative, the determined N NMF basis functions are related to actual physical colors. The NMF basis vectors are integrated with the illuminate conditions and color matching function(s) that approximate the imaging system's sensitivity to generate XYZ color values. These are converted to RGB values which are used to determine the optimal N colors for the N-color based imaging system.

Abstract: A method and system are provided for approximating spectral sensitivities of a particular image sensor, the image sensor having a color filter array positioned over the image sensor. In one example of the method, the method involves measuring spectral sensitivities of a set of image sensors each having a color filter array positioned over the image sensor, calculating mean spectral sensitivities of the set of image sensors for each color within the color filter array, measuring outputs of a particular image sensor when capturing a picture of a plurality of color patches under a first illuminant and calculating spectral sensitivities of the particular image sensor using the mean spectral sensitivities and the output of the particular image sensor. In some embodiments, the method further comprises utilizing the calculated spectral sensitivities to determine outputs of the particular image sensor under a second illuminant.

Abstract: A system and method for effectively performing an image data transformation procedure may include an electronic camera device that is implemented to capture primary image data corresponding to a photographic target. A transformation manager in the electronic camera device may be configured to convert the primary image data into secondary image data by utilizing selectable transformation parameters that are optimized by utilizing an optimization metric to thereby minimize noise characteristics in the secondary image data. The transformation parameters may be stored in parameter lookup tables in the electronic camera device for use by the transformation manager in performing the image data transformation procedure.

Abstract: Fractal decompression and the Retinex algorithm are combined to produce a color constancy method. By using this approach, color constancy and image compression can be achieved simultaneously.

Abstract: A system and method for effectively performing an image data transformation procedure may include an electronic camera device that is implemented to capture primary image data corresponding to a photographic target. A transformation manager in the electronic camera device may be configured to convert the primary image data into secondary image data by utilizing selectable transformation parameters that are optimized by utilizing an optimization metric to thereby minimize noise characteristics in the secondary image data. The transformation parameters may be stored in parameter lookup tables in the electronic camera device for use by the transformation manager in performing the image data transformation procedure.