Bryce Edward Bayer

Bio: Bryce Edward Bayer is an academic researcher from Eastman Kodak Company. The author has contributed to research in topics: Image processing & Signal. The author has an hindex of 7, co-authored 8 publications receiving 2373 citations.

Color imaging array

Abstract: A sensing array for color imaging includes individual luminance- and chrominance-sensitive elements that are so intermixed that each type of element (i.e., according to sensitivity characteristics) occurs in a repeated pattern with luminance elements dominating the array. Preferably, luminance elements occur at every other element position to provide a relatively high frequency sampling pattern which is uniform in two perpendicular directions (e.g., horizontal and vertical). The chrominance patterns are interlaid therewith and fill the remaining element positions to provide relatively lower frequencies of sampling. In a presently preferred implementation, a mosaic of selectively transmissive filters is superposed in registration with a solid state imaging array having a broad range of light sensitivity, the distribution of filter types in the mosaic being in accordance with the above-described patterns.

Image processing method using a collapsed walsh-hadamard transform

Abstract: An improved image processing method uses a modified Walsh-Hadamard transform to remove noise and preserve image structure in a sampled image. Image signals representative of the light value of elements of the image are grouped into signal arrays corresponding to blocks of image elements. These signals are mapped into larger signal arrays such that one or more image signals appear two or more times in each larger array. The larger arrays are transformed by Walsh-Hadamard combinations characteristic of the larger array into sets of coefficient signals. Noise is reduced by modifying--i.e., coring or clipping--and inverting selected coefficient signals so as to recover processed signals--less noise--representative of each smaller signal array. The results exhibit acceptable rendition of low contrast detail while at the same time reducing certain processing artifacts characteristic of the unimproved Walsh-Hadamard block transform.

Transform processing method for reducing noise in an image

Abstract: An improved image processing method reduces noise in a sampled image while minimizing unintended distortion of image features. Image signals are generated representative of the light value of elements of the image. These signals are formed into signal arrays aligned to blocks of image elements. The signal arrays are transformed by a set of 4 by 4 Walsh-Hadamard functions into a corresponding set of coefficient signals. Certain of these coefficient signals represent the difference between the light value of each image element and an average light value over an image region smaller than the block being transformed. By modifying--i.e., coring or clipping--and inverting only these selected coefficient signals, artifacts related to the introduction of "false" edge-like structure are reduced in the reconstructed image. In addition, in a multi-stage processing method, the excluded coefficient signals may represent the input signals to the next stage.

Signal processing for discrete-sample-type-color-video signal

Abstract: Processing circuitry for discrete-sample-type-color-video signals performs an interpolation along a scan row or line to define intermediate signal levels between signal "updates" for the individual primary colors. In a preferred implementation, using green, red, and blue as primary colors, green samples occur more frequently than red or blue and interpolated green samples are specially combined to produce a "slow" green signal that is matched to the frequency ranges of the red and blue signals. The difference between the full green signal and the slow green signal is then used for producing a signal to represent high frequency luminance detail.

Transformation circuit for implementing a collapsed walsh hadamard transform

Abstract: A unit transformation circuit transforms three discrete input signals into a set of transform coefficient signals characteristic of a "collapsed" Walsh-Hadamard transform. The unit transformation circuit includes two tiers of arithmetic networks. In the first tier, a pair of arithmetic networks (36, 38) generates A) first sum and difference signals from the first and second input signals and B) second sum and difference signals from the second and third input signals. Arithmetic networks (40, 42) in the second tier generate a set of coefficient signals from A) the sum of the first and second sum signals B) the sum of the first and second difference signals and C) the difference between the first and second difference signals. The unit transformation circuit forms a fundamental circuit element from which more complex circuits are constructed capable of transforming larger numbers of discrete input signals.

High performance imaging using large camera arrays

Abstract: The advent of inexpensive digital image sensors and the ability to create photographs that combine information from a number of sensed images are changing the way we think about photography. In this paper, we describe a unique array of 100 custom video cameras that we have built, and we summarize our experiences using this array in a range of imaging applications. Our goal was to explore the capabilities of a system that would be inexpensive to produce in the future. With this in mind, we used simple cameras, lenses, and mountings, and we assumed that processing large numbers of images would eventually be easy and cheap. The applications we have explored include approximating a conventional single center of projection video camera with high performance along one or more axes, such as resolution, dynamic range, frame rate, and/or large aperture, and using multiple cameras to approximate a video camera with a large synthetic aperture. This permits us to capture a video light field, to which we can apply spatiotemporal view interpolation algorithms in order to digitally simulate time dilation and camera motion. It also permits us to create video sequences using custom non-uniform synthetic apertures.

Image Processing - Principles and Applications

Abstract: This PDF file contains the editorial “Image Processing: Principles and Applications” for JEI Vol. 15 Issue 03

Adaptive color plane interpolation in single sensor color electronic camera

Abstract: Apparatus is described for processing a digitized image signal obtained from an image sensor having color photosites aligned in rows and columns that generate at least three separate color values but only one color value for each photosite location, and a structure for interpolating color values for each photosite location so that it has three different color values. The apparatus includes a memory for storing the digitized image signal and a processor operative with the memory for generating an appropriate color value missing from a photosite location by the interpolation of an additional color value for such photosite locations from color values of different colors than the missing color value at nearby photosite locations. The processor also includes structure for obtaining Laplacian second-order values, gradient values and color difference bias values in at least two image directions from nearby photosites of the same column and row and for adding the Laplacian second-order values, gradient values and color difference bias values to define a classifier and for selecting a preferred orientation for the interpolation of the missing color value based upon a classifier. Finally, a arrangement is provided for interpolating the missing color value from nearby multiple color values selected to agree with the preferred orientation.

Color plane interpolation using alternating projections

Abstract: Most commercial digital cameras use color filter arrays to sample red, green, and blue colors according to a specific pattern. At the location of each pixel only one color sample is taken, and the values of the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicing; it is one of the important tasks in a digital camera pipeline. If demosaicing is not performed appropriately, images suffer from highly visible color artifacts. In this paper we present a new demosaicing technique that uses inter-channel correlation effectively in an alternating-projections scheme. We have compared this technique with six state-of-the-art demosaicing techniques, and it outperforms all of them, both visually and in terms of mean square error.

Vision system for vehicle

Abstract: A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.