Abstract: A method to select an appropriate window size for local image processing uses two window sizes: a very large but impractical size and a small and practical size. Two images are generated for the desired image processing under both window sizes. The difference of these two images eliminates common non-linear nature of the local image processing and only contains low-frequency artifacts generated under a smaller window size. By taking the Fourier transform of the difference image, an approximated noise spectrum of low-frequency artifacts introduced by the small and practical window size is able to be observed. Alternatively, a frequency analysis of the smaller window size is able to be conducted by calculating the difference of cross-correlation functions and noise spectra under the same two window sizes. An appropriate window size is able to be selected based on such frequency analysis.

Abstract: Two different approaches for reducing the bit depth of the image data so as to reduce the computation and hardware requirement of image patch matching, with minimal loss of matching accuracy are described. Patch matching is able to be implemented in many different ways, but generally involves matching one area of an image with another area of the same image or another area of a different image (e.g. another video frame) through the use of a matching cost function. Transforming the image data to lower bit depth, image processing techniques are able to be implemented to minimize the needed memory and other resources for patch-matching. The complexity/performance trade-off of the approaches are also adjustable so that they are able to be applied for applications with different quality requirements and hardware constraints.

Abstract: A system and method for effectively performing wavelet transforms on incomplete image data includes an image processor that performs a green-pixel transformation procedure on incomplete color pixel matrices. The image processor then rearranges red, blue and transformed green-pixel into four quadrants of contiguous pixels and applies some two dimensional (2D) wavelet thresholding schemes on each quadrant. After thresholding, an inverse procedure is applied to reconstruct the pixel values on the incomplete color pixel matrices. For further de-correlation of image data, the image processor may stack similar image patches in a three dimensional (3D) array and apply incomplete-data wavelet thresholding on the 3D array. The incomplete-data wavelet thresholding procedure may be put in an improved local similarity measurement framework to achieve better performance of image processing tasks. A CPU device typically controls the image processor to effectively perform the image processing procedure.

Abstract: A system and method for effectively performing wavelet transforms on incomplete image data includes an image processor that performs a green-pixel transformation procedure on incomplete color pixel matrices. The image processor then rearranges red, blue and transformed green-pixel into four quadrants of contiguous pixels and applies some two dimensional (2D) wavelet thresholding schemes on each quadrant. After thresholding, an inverse procedure is applied to reconstruct the pixel values on the incomplete color pixel matrices. For further de-correlation of image data, the image processor may stack similar image patches in a three dimensional (3D) array and apply incomplete-data wavelet thresholding on the 3D array. The incomplete-data wavelet thresholding procedure may be put in an improved local similarity measurement framework to achieve better performance of image processing tasks. A CPU device typically controls the image processor to effectively perform the image processing procedure.

Abstract: A method for effectively performing local image similarity measurement is proposed. A system equipped with such a method for effectively performing an image processing task includes an image processor that performs an intermediate-results calculation procedure to calculate intermediate result values that are based upon corresponding pixels of a target patch and one or more similar patches. The image processor typically moves the target patch of the intermediate-results calculation to different locations in a raster order or some other organized order. The image processor then performs an intermediate-results combination procedure by calculating appropriate statistics of the intermediate result values to produce processed pixel values. A processor device typically controls the image processor to effectively perform the image processing tasks including, but not limited to, demosaicing and denoising.

Abstract: A method of adaptive local image similarity measurement based on the L1 distance measure is described. A relationship between distance measures is used to estimate appropriate thresholds for various patch sizes. The choice of patch size depends on the degradations contained in the image and the application. The relation between the similarity measures is established using the distribution of L1 distances for various patch sizes. For larger degradations, similarity measure with a bigger patch size is employed. For lesser imperfections, a smaller patch size produces acceptable results. To keep the computational overhead manageable, the smallest patch size that gives the desired image quality is employed.