Abstract: An image processor merges images from a plurality of cameras including applying spatially-varying gains over images from individual cameras to obtain a consistent effective where the images are stitched together despite the cameras possibly having different camera exposures. The image processor can adjust the camera exposures to improve stitching results. In addition to gain modification, the image processor can also modify the images for other artifacts, such as veiling glare and make those less visible at the seams of images. The image processor also can take into account constraints on gain changes and optimize camera parameters to minimize stitching artifacts. Using a cost function, the image processor can optimize for constraints on camera exposure that would otherwise result in camera exposure differences that would themselves cause visible artifacts.

Abstract: An adaptive filter is implemented by a computer (10) processing an input signal using a recursive least squares lattice (RLSL) algorithm (12) to obtain forward and backward least squares prediction residuals. A prediction residual is the difference between a data element in a sequence of elements and a prediction of that element from other sequence elements. Forward and backward residuals are converted at (14) to interpolation residuals which are unnormalized Kalman gain vector coefficients. Interpolation residuals are normalized to produce the Kalman gain vector at (16). The Kalman gain vector is combined at (18) with input and reference signals x(t) and y(t), which provides updates for the filter coefficients or weights to reflect these signals as required to provide adaptive filtering.

Abstract: An adaptive filter is implemented by a computer (10) processing an input signal using a recursive least squares lattice (RLSL) algorithm (12) to obtain forward and backward least squares prediction residuals. A prediction residual is the difference between a data element in a sequence of elements and a prediction of that element from other sequence elements. Forward and backward residuals are converted at (14) to interpolation residuals which are unnormalised Kalman gain vector coefficients. Interpolation residuals are normalised to produce the Kalman gain vector at (16). The Kalman gain vector is combined at (18) with input and reference signals x(t) and y(t), which provides updates for the filter coefficients or weights to reflect these signals as required to provide adaptive filtering.