Apple Inc.

Wu Cheng

Hao Sun

Farhan A Baqai

Gijesh Varghese

Morten Poulsen

Tak Shing Wong

Alok Deshpande

Marius Tico

Jun Hu

Anita Nariani Schulze

Justin Michael Faryabi-Allen

Sheng Lin

Todd Stephen Sachs

Maxim W Smirnov

Su Wang

Richard David Seely

Claus Moelgaard

Giancarlo Todone

Richard J Shields

Srivani Pinneli

Fabio Ricardi

Farhan A Bagai

An adaptive approach to image bracket determination and a more memory-efficient approach to image fusion, which are designed to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions, are described. An incoming preview image stream may be obtained from an image capture device. When a capture request is received, an analysis may be performed on an image from the preview image stream that has a predetermined temporal relationship to the image capture request. Based on the analysis, a set of images (and their respective capture parameters, e.g., exposure time) may be determined for the image capture device to capture. As the determined set of images are captured, they may be registered and fused in a memory-efficient manner that, e.g., places an upper limit on the overall memory footprint of the registration and fusion operations—regardless of how many images are captured in total.

Adaptive image bracket determination

In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

Gradient-based noise reduction

Devices, methods, and computer-readable media are disclosed, describing an adaptive, subject-aware approach for image bracket selection and fusion, e.g., to generate high quality images in a wide variety of capturing conditions, including low light conditions. An incoming image stream may be obtained from an image capture device, comprising images captured using differing default exposure values, e.g., according to a predetermined pattern. When a capture request is received, it may be detected whether one or more human or animal subjects are present in the incoming image stream. If a subject is detected, an exposure time of one or more images selected from the incoming image stream may be reduced relative to its default exposure time. Prior to the fusion operation, one of the selected images may be designated a reference image for the fusion operation based, at least in part, on a sharpness score and/or a blink score of the image.

Subject-aware low light photography

Devices, methods, and computer-readable media are disclosed describing an adaptive approach for image bracket selection and fusion, e.g., to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV&#x2212; images, EV+ images, long exposure (or synthetic long exposure) images, EV0/EV&#x2212; image pairs, etc., which are received according to a particular pattern. When a capture request is received, a set of rules and/or a decision tree may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. A noise reduction process may optionally be performed on the selected images before (or after) the registration and fusion operations.

Unified bracketing approach for imaging

In one embodiment, a method includes obtaining an image comprising a plurality of pixels. The method includes determining, for a particular pixel of the plurality of pixels, a feature value. The method includes selecting, based on the feature value, a set of selected pixels from a set of candidate pixels in an image region surrounding the particular pixel. The method includes denoising the particular pixel based on the set of selected pixels.

Noise reduction with classification-based contrast preservation

Devices, methods, and computer-readable media describing an adaptive approach to reference image selection are disclosed herein, e.g., to generate fused images with reduced motion distortion. More particularly, an incoming image stream may be obtained from an image capture device, which image stream may comprise a variety of different image captures, e.g., including 'image frame pairs' (IFPs) that are captured consecutively, wherein the images in a given IFP are captured with differing exposure settings. When a capture request is received at the image capture device, the image capture device may select two or more images from the incoming image stream for fusion, e.g., including at least one IFP. In some embodiments, one of the images from the at least one IFP will be designated as the reference image for a fusion operation, e.g., based on a robust motion detection analysis process performed on the images of the at least one IFP.

Robust image motion detection using scene analysis and image frame pairs

Devices, methods, and computer-readable media describing an adaptive approach for image selection, fusion, and noise reduction, e.g., to generate low noise and high dynamic range (HDR) images with improved motion freezing in a variety of capturing conditions. An incoming image stream may be obtained from an image capture device, wherein the image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV&#x2212; images, EV+ images. When a capture request is received, a set of rules may be used to evaluate one or more capture conditions associated with the images from the incoming image stream and determine which two or more images to select for a fusion operation. The fusion operation may be designed to adaptively fuse the selected images, e.g., in a fashion that is determined to be optimal from a noise variance minimization standpoint. A fusion-adaptive noise reduction process may further be performed on the resultant fused image.

Fusion-adaptive noise reduction

Embodiments relate to biasing an image noise filter to reduce edge and texture blurring of image data. Pixel values used to determine photometric coefficients for a bilateral filter are modified by offset values. The offset value for a pixel value is determined by applying a high pass filter to the pixel (referred to as the center pixel) and neighboring pixels of the center pixel. By adding the offset value to the center pixel value, the pixel value difference between the neighboring pixels and the center pixel becomes smaller for pixels on the same side of an edge as the center pixel. Thus, pixels on the same side of the edge get more weight in the bilateral noise filter. Conversely, pixels on the opposite side of the edge as the center pixel get less weight in the bilateral filter. As a result, the biased bilateral filter reduces blurring of edges and increases preservation of texture in the image data.

Growing community of inventors

Millbrae, CA, United States of America

Wu Cheng