Patent Description:
Some imaging devices use two or more cameras to implement a zoom function. In these devices, each camera includes its own associated circuitry, lens and sensors. Accordingly, the output of these cameras, even when imaging the same scene, can differ in image parameters, for example in field of view and color. Depending on the zoom command received from a user's input, the imaging device can activate an appropriate camera and direct its output to an image preview screen. However, when the imaging device switches the active camera, a user can undesirably perceive a sudden change in the preview screen while composing an image scene in the preview mode of the imaging device. Consequently, systems and methods are needed to implement a seamless zoom where a user can compose an image scene and can input zoom commands without perceiving an undesirable shift in the preview mode of the imaging device when the imaging device switches between the active cameras.

<CIT> describes a dual-aperture zoom digital camera operable in both still and video modes. The camera includes Wide and Tele imaging sections with respective lens/sensor combinations and image signal processors and a camera controller operatively coupled to the Wide and Tele imaging sections. The Wide and Tele imaging sections provide respective imaging data. The controller is configured to combine in still mode at least some of the Wide and Tele image data to provide a fused output image from a particular point of view, and to provide without fusion continuous zoom mode output images, each output image having a given output resolution, wherein the video mode output images are provided with a smooth transition when switching between a lower zoom factor (ZF) value and a higher ZF value or vice versa, and wherein at the lower ZF the output resolution is determined by the Wide sensor while at the higher ZF value the output resolution is determined by the Tele sensor.

<CIT> describes systems and methods for image data fusion that include providing first and second sets of image data corresponding to an imaged first and second scene respectively. The scenes at least partially overlap in an overlap region, defining a first collection of overlap image data as part of the first set of image data, and a second collection of overlap image data as part of the second set of image data. The second collection of overlap image data is represented as a plurality of image data subsets such that each of the subsets is based on at least one characteristic of the second collection, and each subset spans the overlap region. A fused set of image data is produced by an image processor, by modifying the first collection of overlap image data based on at least a selected one of, but less than all of, the image data subsets.

The scope of the present invention is defined by the scope of the appended claims. Any embodiments which do not fall within the scope of the claims are examples which are helpful for understanding the invention, but do not form a part of the invention.

Embodiments of the disclosure relate to systems and techniques for implementing a seamless zoom function in a multi-camera device, for example, a device having two cameras. "Camera" as used herein refers to a set of image sensing components that typically include a sensor array (or "sensor") and one or more optical components (for example, one or more lenses or light re-directing components, sometimes referred to herein as "optics" or "set of optics") which light from a target scene propagates through before it reaches the sensor array. In some embodiments, a multi-camera device may include multiple cameras, each including a set of optics and a corresponding sensor array. In other examples of devices with multiple cameras, a particular set of optics may correspond to two or more sensors, that is, provide light to two or more sensors. Some of the examples described herein describe a multi-camera device with asymmetric lenses/cameras, for example, a wide-angle camera and a telephoto camera, however, other examples of multi-camera devices are also contemplated, and this disclosure is not intended to be limited to the particular examples described.

When viewing images generated by an imaging device having two or more cameras, switching between the cameras can cause undesirable image aberrations which may be perceived by a user viewing the images on a display. For example, the images generated by the two cameras for the same target scene may be different in spatial orientation and appear to have colors. Methods and systems of this disclosure address switching between cameras, for example, a two camera system where a first camera is configured to capture wide angle images of a target scene and includes a wide angle lens, and a second camera configured to capture telephoto images of the same target scene and includes a telephoto lens. A desired characteristic of a multiple camera system is to switch from a wide-angle camera view to a telephoto camera view of a target scene when a zoom-in command is received and the switching is seamless, that is, it is not able to be perceived by a user, or such perception of the switch between cameras is minimized.

With multiple cameras on an imaging apparatus, the images captured may be at different viewpoints due to the physical location of the cameras being different. For example, the cameras may be separated by a distance of one (<NUM>) cm on the multi-camera device. To align the images such that when they are viewed the differences that exist in the raw images are imperceptible, the images may be spatially aligned, the color (and intensities) of the images may be aligned, and then the images may be presented for viewing in a fusion process that further minimizes the perceptible difference between images generated by two (or more) different cameras. In some examples, based on the distance to the object being viewed, either images from wide-angle camera are presented on a display, images from a telephoto camera are presented on the display, or a fused image is presented on the display, where one portion of the fused image is based on data from the wide-angle camera and another portion of the fused image is based on data received by the telephoto camera. Such a fused image is based on transforming images from a first camera (e.g., a wide-angle camera) to match images from a second camera (e.g., a telephoto camera), or transforming images from a second camera (e.g., a telephoto camera), to match images from a first camera (e.g., a wide-angle camera), or transforming images from one, or both, a first and second camera to generate fused images, according to various embodiments.

The systems and methods may include operations on "still" preview images or video preview images. In some embodiments, for still images, a preview image may include an image formed from two cameras. For example, the preview image may have an outer portion that is from a wide-angle camera and a center portion that is from a telephoto camera, the portions being stitched together to appear seamless (or nearly so) to a viewer. That is, the portion of a wide-angle image of a target scene that is captured by a telephoto camera is replaced by the corresponding portion of a telephoto image of the target scene. In such an example, spatial alignment and photometric alignment operations transform pixels of one or both images to provide the seamless composite image. To provide a seamless composite image, the portion of the borders of the two images may registered. The photometric transform of pixels in the border region allow the borders to be seamless when the image is presented in a preview image.

In some embodiments, static calibration in the factory may be performed first, which includes identifying corresponding regions in both set of images and estimating a mathematical function (transform) that maps the pixels from an image captured by one camera to pixels in the an image captured by another camera. The calibration may include detecting keypoints in the two images being matched, matching the keypoints between the images, and estimating parameters of that matching to generate a spatial transform that defines a spatial relationship between corresponding pixels in images from the first camera and images from the second camera. The parameters for the spatial transform may include, for example, a scale shift, an angular rotation, or a shift in the horizontal or vertical direction.

After the images from the first and second cameras are spatially aligned, photometric alignment may be performed to match corresponding portions of the images in color and intensity. In some embodiments, the images from the first camera (e.g., a wide-angle camera) and from the second camera (e.g., a telephoto camera) are partitioned into regions, and histogram equalization is performed in multiple regions in the images. For example, the images from the first and second cameras can be divided into N regions and local histograms are computed for each of the regions. The histograms of corresponding regions may be equalized for intensity of color channels that make up the image. Adjacent regions are interpolated so boundaries of the regions are made seamless. In some examples, photometric alignment generates photometric transformation parameters that can be stored and then retrieved and applied to subsequent images of the first and/or second camera to photometrically align the images. The transformation parameters can be adaptive. In other words, the transformation parameters may be dynamically updated and re-stored for later use as additional images from the from the first camera and the second camera are spatially and photometrically determined.

<FIG> illustrates an example of different fields of view corresponding to different cameras of a multi-camera device <NUM>. In this example, the multi-camera device <NUM> includes a first camera <NUM> having optics that includes a wide-angle lens, and a second camera <NUM> having optics that includes a telephoto lens. The first camera <NUM> and the second camera <NUM> are positioned to both have a field-of-view (FOV) that includes the same target scene but each with a different angle of view of the target scene. In this example implementation, the first camera <NUM> (also referred to as wide-angle camera <NUM>) has an angle of view θ<NUM>, and the second camera <NUM> (also referred to as telephoto camera <NUM>) has an angle of view θ<NUM>. The angle of view θ<NUM> for the wide-angle camera <NUM> is larger than the angle of view θ<NUM> for the telephoto camera <NUM>. Thus, the wide-angle camera <NUM> produces images with a "wider" field of view <NUM> compared to the telephoto camera <NUM> which produces images having a "narrower" field of view <NUM>. As illustrated in <FIG>, the wide-angle camera <NUM> of the multi-camera device <NUM> can be positioned a known distance "b" from the telephoto camera <NUM>, as will be discussed further below.

In an example implementation, the first camera <NUM> is the main camera and has a wide angle lens, for example, having a focal length of <NUM>. The angle of the FOV of the first camera <NUM> is <NUM> degrees. The sensor of the first camera <NUM> includes an array of <NUM> pixels along a length dimension, having <NUM> pixels, have a <NUM>:<NUM> aspect, and have autofocus functionality. In an example implementation, the second camera is an auxiliary camera and has telephoto lens having a focal length of <NUM>. In this example implementation, the angle of view of the second camera <NUM> is <NUM> degrees, and the sensor of the second camera <NUM> includes an array of <NUM> pixels along a length dimension, having <NUM> pixels, has a <NUM>:<NUM> aspect, and also has auto-focus functionality.

Images captured by each camera of the multi-camera device <NUM> can be provided to a display device for viewing by a user. When the multi-camera device <NUM> is aimed at a target scene and receives a zoom-in command, the multi-camera device <NUM> may switch from the wide-angle camera <NUM> to the telephoto camera <NUM> while a user is previewing the target scene on a display and/or when images are being captured by the multi-camera device <NUM>. Because a telephoto lens has a narrower field of view than a wide-angle lens and may have a different sensor and different imaging parameters, the user may perceive, and in video the multi-camera device may capture, a sudden undesirable change in preview images shown on the display. Similarly, a zoom-out command may cause the multi-camera device to switch from the telephoto camera <NUM> to the wide-angle camera <NUM>, and because a telephoto lens has a narrower field of view than a wide-angle lens, a perceptible sudden, undesirable change in the images maybe seen on the display device and captured in images captured by the multi-camera device <NUM>.

<FIG> illustrate an example of a multi-camera device <NUM> imaging a scene <NUM>, where a display <NUM> shows a preview of the scene <NUM> in response to zoom commands <NUM>, according to some embodiments. In this example, the multi-camera device <NUM> includes a first camera with a wide-angle lens (wide-angle camera) <NUM> and a second camera with a telephoto lens (telephoto camera) <NUM>. The illustrated multi-camera device <NUM> also includes a display <NUM>. The display <NUM> can be used to view images before they are captured and stored in memory (i.e., preview images), or images that are being captured and stored in memory. In <FIG>, region <NUM> denotes the field of view of the telephoto camera <NUM> while the region <NUM> can denote the field of view of the wide-angle camera <NUM>. As the multi-camera device <NUM> processes a zoom-in command, the display <NUM> correspondingly shows a zoomed-in representation of the scene <NUM> as illustrated by <FIG>. Zoom sliders <NUM> illustrates the level or range of zoom command input by a user.

<FIG> illustrates when the zoom-in command is indicative of a zoom level where the output of an image captured using wide-angle camera <NUM> can be provided to the display <NUM> and fills the display. In <FIG>, the field of view of the telephoto camera <NUM> is denoted by region <NUM>. The actual boundaries of the region <NUM> may be imperceptible (as displayed) to the user. In <FIG>, a received zoom-in command indicating a zoom level where the multi-camera device <NUM> activates the telephoto camera <NUM> and uses images captured using the telephoto camera <NUM> to display a more zoomed-in image. The wide angle camera <NUM> and the telephoto camera <NUM> may produce images that differ in color and spatial alignments in the region <NUM>. In <FIG>, when the imaging limits of the wide-angle camera <NUM> are reached (or are at some threshold value near that point) the multi-camera device <NUM> may produce a fused (or combined) image, combining the output of the wide-angle camera <NUM> and the telephoto camera <NUM>. That is, producing a fused image where the center portion of the fused image corresponds to the region <NUM> containing image data from the telephoto camera <NUM> and the remaining portions between the regions <NUM> and <NUM> contain image data from the wide-angle camera <NUM>. To the user the preview images shown on the display <NUM> in <FIG> may appear similar or identical. Registration may be performed in border areas in the fused image, where portions of each of the two images are adjacent to each other, to ensure a seamless (or nearly so) fused image. In <FIG>, if the multi-camera device <NUM> continues receive a zoom-in command, the multi-camera device <NUM> can continue to zoom-in the region <NUM> and the image data for previewing images of the target scene can be already available as a result of the image fusion operation performed earlier.

Conversely, in <FIG>, the multi-camera device <NUM> may begin receive a zoom-out command, and in zooming out the limits of the image data illustrated in <FIG> from the telephoto camera <NUM> may be reached. Between <FIG>, the multi-camera device <NUM> can perform another image fusion operation, producing an image combining the output of the wide-angle camera <NUM> and the telephoto camera <NUM> to produce an image where the center portion corresponding to the region <NUM> contains image data from the telephoto camera <NUM> and the remaining portions between the regions <NUM> and <NUM> contain image data from the wide-angle camera <NUM>.

If the spatial transform and photometric transform described herein are not performed, a user may see an abrupt change in the display <NUM> going from <FIG> when a zoom-in command is received at or near the outer limit of the wide-angle camera <NUM> and the image displayed changes from the wide-angle camera <NUM> to the telephoto camera <NUM>. Additionally, the user may see an abrupt change in the display <NUM> going from <FIG> when a zoom-out command is received and the image displayed is at or near the limit of the telephoto camera <NUM>. To address these issues, a transitional image may be produced combining spatially aligned and photometrically aligned images of both the wide-angle camera <NUM> and the telephoto camera <NUM> such that switching between the two cameras can be performed in a seamless manner unperceivable or nearly unperceivable to the user.

As discussed above, the images produced by the wide-angle camera <NUM> and telephoto camera <NUM> can be different in spatial alignment and/or photometric characteristics (e.g., color, intensity). In some embodiments, spatial alignment and/or color intensity matching or equalization can be performed to produce a smooth transform of images from the wide-angle camera <NUM> and telephoto camera <NUM>. Image alignment functionality may include image spatial alignment along with intensity equalization in region matching for image alignment. Each image in a set of images can depict substantially the same image scene, for example from different viewpoints, in different lighting, or in different portions of the electromagnetic spectrum. Each of the images in the set can be divided into a number of regions including blocks of pixels. The regions can be matched to determine regions depicting corresponding features between the images, that is, to determine which regions in the images depict the same feature. The regions depicting corresponding features can be matched spatially and as to intensity. The spatial or intensity correspondence between regions depicting corresponding features can permit accurate matching using the regions, leading, for example, to accurate downstream image alignment and/or depth map construction.

In some embodiments, the regions can be determined at least partly based on identifying distinctive features, referred to as keypoints, in each image in the set of images. Keypoints may be selected and/or processed such that they are invariant to image scale changes and/or rotation and provide robust matching across a substantial range of distortions, changes in point of view, and/or noise. The region location, shape, and size can be determined based, for example, on the location, shape, and size of the extracted features. Spatial and/or intensity equalization between corresponding regions can adapt to local structure content such as the shape of keypoints. Accordingly, the effects of spatial and/or intensity variations on keypoint matching can be mitigated or eliminated by region matching and/or equalizing intensity of corresponding regions after keypoint detection.

Spatial alignment or equalization of intensity values between corresponding regions can accommodate the structure of keypoints included in the regions. In some examples, the histogram of each corresponding region can be analyzed to determine spatial intensity variation, and a spatial mapping between the intensities of the corresponding regions can be performed to provide equalized intensity that is adapted to local structure content such as distinctive features. For example, after determining an equalization function based on histogram analysis of the blocks in a pair of images, intensity values in a first image can be mapped to the intensity values in a second image one such that the first image is transformed to have a histogram most closely resembling or matched to a histogram of the second image. All of the determined regions may look very similar in terms of intensity values, and accordingly can be identified by subsequent processing as corresponding regions in each image even though they were produced with different sensors, optics, and/or light wavelengths.

Although aspects of the embodiments described in this disclosure will focus on region matching within the context of stereo image pairs, this is for purposes of illustration and is not intended to limit the use of the spatial alignment and local intensity equalization techniques described herein. Spatial and/or intensity alignment using region matching for non-stereo image pairs can be also performed more accurately using the spatial alignment and/or local intensity equalization techniques described herein. For example, spatial alignment and/or local intensity equalization according to the embodiments described herein can provide for more accurate multispectral image alignment - matching images from different portions of the spectrum of light - such as aligning a near-infrared (NIR) image and a visible color (e.g., RGB) image captured of the same image scene. Spatial alignment and/or local intensity equalization can also provide more accurate spectroscopic image alignment, for example for aligning sets of images taken at different wavelengths by optical systems using diffraction grating to perform spectroscopy. The spatial alignment and/or local intensity equalization techniques described herein can be used to align a pair of images or a set of three or more images in various embodiments. Further, the spatial alignment and/or local intensity equalization techniques described herein are not limited to alignment by region matching, and can be incorporated into any image alignment or rectification technique.

<FIG> is a block diagram illustrating an example of an embodiment of a multi-camera device <NUM> having seamless zoom capability. In this example, the multi-camera device <NUM> includes an image processor <NUM> coupled to two of more cameras, in this example, a first camera <NUM> and a second camera <NUM>. The multi-camera device <NUM> also may include a working memory <NUM>, storage <NUM>, a display <NUM>, and a memory <NUM>, all coupled to and in communication with the image processor <NUM>. In some embodiments including the illustrated embodiment in <FIG>, components of the multi-camera device <NUM> including the display <NUM> and storage <NUM> may be coupled to and/or in communication with the image processor <NUM> via a device processor <NUM>. In this example, memory <NUM> includes modules having instructions to configure the image processor to perform various operations including seamless zoom functionality.

In various embodiments, the multi-camera device <NUM> may be a cell phone, digital camera, tablet computer, personal digital assistant, or the like. A plurality of imaging applications may be available to the user on multi-camera device <NUM>. These applications may include traditional photographic and video applications, high dynamic range imaging, panoramic photo and video, stereoscopic imaging such as 3D images or 3D video, or multispectral imaging, to name a few. The multi-camera device <NUM> as illustrated includes the first camera <NUM> and second camera <NUM> for capturing external images. The first camera <NUM> and second camera <NUM> each may include various components that are not explicitly shown in <FIG> for clarity, including for example a sensor, a lens assembly, and autofocus module. The first camera <NUM> and second camera <NUM> may be charge coupled devices (CCD) or complementary metal oxide semiconductors (CMOS) in some embodiments. The first camera <NUM> and second camera <NUM> are configured with different components (for example, optics, sensors) and thus produce images that are formed based on their own particular optics and sensor. Thus, the target image scene may be captured as a set of images in which first camera <NUM> captures an image A according to the sensor's intensity response and first camera second camera <NUM> captures an image B according to the sensor's intensity response. Additionally, the captured images A and B can differ in spatial alignment, for example, due to the physical offset between the cameras, roll, pitch and yaw between the cameras or lenses in sensor assemblies A and B. Although two cameras are shown (i.e., first camera <NUM> and second camera <NUM>), this is for purposes of illustration and is not intended to limit the type of system which can implement the spatial alignment and intensity equalization techniques described herein. In other embodiments, three or more cameras can capture a set of images of a target scene, the set of images exhibiting at least some spatial misalignment and/or local intensity variation. In still other embodiments, a single cameras can capture a set of images of a target scene, the set of images exhibiting at least some local intensity variation. In some embodiments, one or more of the cameras may not be part of the multi-camera device <NUM>, instead information from one or more cameras is provided to the multi-camera device <NUM> for processing. For example, the cameras may be part of another imaging system, and information from such a system may be provided to be processed using functionality described for the multi-camera device <NUM>. In some embodiments, such information is first stored, and then provided to the multi-camera device <NUM> for processing. The number of sensor assemblies may be increased or decreased according to the needs of the imaging system <NUM>. The first camera <NUM> and second camera <NUM> may be coupled to the image processor <NUM> to transmit captured images to the image processor <NUM>.

The image processor <NUM> may be configured to perform various processing operations on received image data including a number of images of the image scene in order to output an accurately aligned image set, as will be described in more detail below. Image processor <NUM> may be a general purpose processing unit or a processor specially designed for imaging applications. Examples of image processing operations include cropping, scaling (e.g., to a different resolution), image stitching, image format conversion, color interpolation, color processing, image filtering (e.g., spatial image filtering), lens artifact or defect correction, etc. Image processor <NUM> may, in some embodiments, comprise a plurality of processors. Certain embodiments may have a processor dedicated to each image sensor. Image processor <NUM> may be one or more dedicated image signal processors (ISPs) or a software implementation of a processor.

Image processor <NUM> is connected to a memory <NUM> and a working memory <NUM>. In the illustrated example, the memory <NUM> stores capture control module <NUM>, intensity alignment module <NUM>, spatial alignment module <NUM>, state machine module <NUM> and operating system <NUM>. These modules include instructions that configure the image processor <NUM> and/or device processor <NUM> to perform various image processing and device management tasks. Working memory <NUM> may be used by image processor <NUM> to store a working set of processor instructions contained in the modules of memory <NUM>. Alternatively, working memory <NUM> may also be used by image processor <NUM> to store dynamic data created during the operation of multi-camera device <NUM>.

As described above, the image processor <NUM> may be configured, or controlled, by several modules stored in the memory, for example, memory <NUM>. The capture control module <NUM> may include instructions that configure the image processor <NUM> to adjust the focus position of first camera <NUM> and second camera <NUM>. Capture control module <NUM> may further include instructions that control the overall image capture functions of the multi-camera device <NUM>. For example, capture control module <NUM> may include instructions that call subroutines to configure the image processor <NUM> to capture raw image data of a target image scene using the first camera <NUM> and second camera <NUM>. Capture control module <NUM> may then call the spatial alignment module <NUM> and/or intensity alignment module <NUM> to perform spatial alignment and/or local intensity equalization on the images captured by the first camera <NUM> and second camera <NUM>, and to output aligned image data to image processor <NUM>. Capture control module <NUM> may also call the spatial alignment module <NUM> and/or intensity alignment module <NUM> to perform spatial alignment and/or intensity equalization on raw image data in order to output a preview image on display <NUM> of a scene to be captured in some embodiments, and to update the preview image at certain time intervals or when the scene in the raw image data changes.

Spatial alignment module <NUM> may include instructions that configure the image processor <NUM> to perform spatial alignment on captured image data. For example, each of the first camera <NUM> and second camera <NUM> may capture an image depicting the target scene according to each camera's different parameters and characteristics. As discussed above, images generated of the same target scene from the first camera <NUM> and second camera <NUM> may differ due to discrepancies in sensor gains, roll-offs, pitch, yaw, sensitivity, field of view, white balance, geometric distortion, and noise sensitivities, differences between the lenses in the first camera <NUM> and the second camera <NUM>, and on-board image signal conditioning. In order to perform accurate spatial alignment of the images, spatial alignment module <NUM> may configure the image processor <NUM> to detect corresponding features between the images from the first camera <NUM>, estimate an appropriate transformation (or mapping between the corresponding regions) and perform region matching producing images which can be accurately juxtaposed on top of each other. Additionally, the spatial alignment module <NUM> may configure the image processor <NUM> to align the two images even when corresponding features between images cannot be detected.

Spatial alignment module <NUM> can include feature detector <NUM> including instructions that configure the image processor <NUM> to detect distinctive features, or keypoints, in the image data. Such features can correspond to points in the images that can be matched with a high degree of accuracy. For example, distinctive features may be characterized at least partly by the presence or sharpness of edges or lines, corners, ridges, or blobs differing in properties, for example, size, shape, dimension, brightness or color compared to surrounding pixel regions. Generally, object or feature recognition may involve identifying points of interest (also called keypoints) in an image and/or localized features around those keypoints for the purpose of feature identification. An object or feature may be characterized by descriptors identifying one or more keypoints. Keypoints can be identified by any known feature detection technique, e.g., sum of squared differences, Laplacian of Gaussian (LoG), difference of Gaussian (DoG), and determinant of the Hessian (DoH), to name a few.

Feature detector <NUM> can also include instructions that configure the image processor <NUM> to partition the image data into regions including pixel blocks based at least partly on the identified keypoints. The pixel block location, shape, and size can be determined based, for example, on the location, shape, and size of the identified keypoints. In some embodiments such as some stereoscopic alignment applications, the feature detector <NUM> can include instructions that configure the image processor <NUM> to limit pixel block size to larger than a disparity value and/or smaller than a roll-off variation value.

Spatial alignment module <NUM> can also include a matching module <NUM>, which includes instructions that configure the processor <NUM> to estimate and apply one or more transformations to match the corresponding regions of one or more images generated by the first camera <NUM> and the second camera <NUM>.

Intensity alignment module <NUM> may include instructions that configure the image processor <NUM> to perform image intensity alignment (which also may be referred to photometric alignment) using local intensity equalization techniques on captured image data. For example, each of the first camera <NUM> and second camera <NUM> may capture an image depicting the target scene according to each sensor's intensity response. As discussed above, the intensity responses may differ due to discrepancies in sensor gains, roll-offs, sensitivity, field of view, white balance, geometric distortion, and noise sensitivities, among other things, due to differences in the lenses and/or sensors first camera <NUM> and second camera <NUM>, and on-board image signal conditioning. In order to perform accurate intensity alignment of the images despite local intensity variations between the images, intensity alignment module <NUM> may configure the image processor <NUM> to partition the images into a number of regions, equalize local intensity values of corresponding regions, and perform region matching using the intensity-equalized regions.

For instance, intensity alignment module <NUM> can include feature detector <NUM> including instructions that configure the image processor <NUM> to detect distinctive features, or keypoints, in the image data. Such features can correspond to points in the images that can be matched with a high degree of accuracy. For example, distinctive features may be characterized at least partly by the presence or sharpness of edges or lines, corners, ridges, or blobs differing in properties, for example, size, shape, dimension, brightness or color compared to surrounding pixel regions. Generally, object or feature recognition may involve identifying points of interest (also called keypoints) in an image and/or localized features around those keypoints for the purpose of feature identification. An object or feature may be characterized by descriptors identifying one or more keypoints. Keypoints can be identified by any known feature detection technique, e.g., sum of squared differences, Laplacian of Gaussian (LoG), difference of Gaussian (DoG), and determinant of the Hessian (DoH), to name a few.

Intensity alignment module <NUM> can also include histogram module <NUM>, which includes instructions that configure the processor <NUM> to generate and analyze histograms of the regions and generate an intensity equalization function based at least partly on the histogram analysis. Histogram module <NUM> can include instructions that configure the processor <NUM> to determine the probability mass function (PMF) of each block, to sum the mass in the PMF to compute the cumulative mass function (CMF), and to use the CMF to map intensity values from pixels in the image captured by first camera <NUM> to pixels in the image captured by second camera <NUM> (or vice versa). Accordingly, the intensity equalization between corresponding regions can adapt to local structure content such as the shape of keypoints.

Intensity alignment module <NUM> can also include matching module <NUM>, which includes instructions that configure the processor <NUM> to perform region matching using the intensity-equalized image data generated by the histogram module <NUM>. Due to the local adaptive intensity equalization, the corresponding keypoint regions may look very similar to one another in terms of intensity values, enabling highly accurate matching around the keypoint structures, even in images exhibiting spatially varying intensity.

State machine module <NUM> configures the image processor <NUM> and/or the device processor <NUM> to turn the first camera <NUM> and the second camera <NUM> on and off, to take image feeds from the first and second cameras <NUM>, <NUM>, to show on a display portions or all of the images from the first and second cameras <NUM>, <NUM>. The resulting state as dictated by the state machine <NUM> may cause image transformation parameters to be stored, or retrieved from storage, that may be used to reapply the transformation parameters or the inverse of the transformation depending on the zoom command input by a user of the multi-camera device <NUM>.

Operating system module <NUM> may configure the image processor <NUM> to manage the working memory <NUM> and the processing resources of multi-camera device <NUM> for various operational tasks. For example, operating system module <NUM> may include device drivers to manage hardware resources such as the first camera <NUM> and second camera <NUM>. Therefore, in some embodiments, instructions contained in the image processing modules discussed above may not interact with these hardware resources directly, but instead interact through standard subroutines or APIs located in operating system module <NUM>. Instructions within operating system module <NUM> may then interact directly with these hardware components. Operating system module <NUM> may further configure the image processor <NUM> to share information with device processor <NUM>.

Device processor <NUM> may be configured to control the display <NUM> to display the captured image, or a preview of the captured image, to a user. The display <NUM> may be external to the multi-camera device <NUM> or may be part of the multi-camera device <NUM>. The display <NUM> may also be configured to provide a view finder displaying a preview image for a use prior to capturing an image, or may be configured to display a captured image stored in memory or recently captured by the user. The display <NUM> may comprise an LCD or LED screen, and may implement touch sensitive technologies.

Device processor <NUM> may write data to storage module <NUM>, for example data representing captured images, image alignment data, intensity value data, and the like. While storage module <NUM> is represented graphically as a traditional disk device, those with skill in the art would understand that the storage module <NUM> may be configured as any storage media device. For example, the storage module <NUM> may include a disk drive, such as a floppy disk drive, hard disk drive, optical disk drive or magneto-optical disk drive, or a solid state memory such as a FLASH memory, RAM, ROM, and/or EEPROM. The storage module <NUM> can also include multiple memory units, and any one of the memory units may be configured to be within the multi-camera device <NUM>, or may be external to the multi-camera device <NUM>. For example, the storage module <NUM> may include a ROM memory containing system program instructions stored within the multi-camera device <NUM>. The storage module <NUM> may also include memory cards or high speed memories configured to store captured images which may be removable from the camera.

Although <FIG> depicts a device having separate components to include a processor, imaging sensor, and memory, one skilled in the art would recognize that these separate components may be combined in a variety of ways to achieve particular design objectives. For example, in an alternative embodiment, the memory components may be combined with processor components to save cost and improve performance. Additionally, although <FIG> illustrates two memory components, including memory component <NUM> including several modules and a separate working memory <NUM>, other embodiments may utilize different memory architectures. For example, a design may utilize ROM or static RAM memory for the storage of processor instructions implementing the modules contained in memory <NUM>. The processor instructions may be loaded into RAM to facilitate execution by the image processor <NUM>. For example, working memory <NUM> may comprise RAM memory, with instructions loaded into working memory <NUM> before execution by the processor <NUM>.

<FIG> is a block diagram illustrating an example of the overview of a process and/or system <NUM> to seamlessly display an image, or a series of images, of a target scene that represent the field-of view of a multi-camera device as it is being zoomed-in or zoomed-out, the displayed image including data from one or more of the cameras of the multi-camera device. In such a process/system, the images from the multiple cameras are processed such that when they are displayed, there is not a perceivable difference to user when the image displayed is being provided from one camera or the other, or both, despite each camera having different imaging characteristics. In the example of <FIG>, the multi-camera device has two cameras. In other examples, the multi-camera device can have three or more cameras. Each of the illustrated blocks of process/system <NUM> is further described herein.

Image A <NUM> from a first camera and image B <NUM> from a second camera are received and static calibration <NUM> is performed. Although referred to for convenience as image A <NUM> and image B <NUM>, image A <NUM> may refer to a series of images from the first camera of the multi-camera device. Such series of images may include "still" images or a series of images captured as video. Similarly, image B <NUM> may refer to a series of images from the first camera of the multi-camera device. Such series of images may include "still" images or a series of images captured as video. Static calibration <NUM> may be performed using a known target scene, for example, a test target. In some examples, static calibration may be performed "at the factory" as an initial calibration step of a multi-camera device. Aspects of static calibration are further described, for example, in <FIG>. Parameters determined from static calibration <NUM> may be stored in memory to be subsequently used for spatial alignment <NUM> and/or for photometric alignment <NUM>.

In this example, spatial alignment <NUM> further spatially aligns image A and image B, mapping pixels from image A to corresponding pixels of image B. In other words, spatial alignment <NUM> may determine a pixel or a plurality of pixels in image A that represent the same feature as a corresponding pixel of pixels in image B. Certain aspect of spatial alignment are further described in reference to <FIG>.

The process/system <NUM> also includes photometric alignment <NUM>, which is also referred to herein as intensity alignment. Photometric alignment <NUM> determines transform parameters that indicate a color and/or an intensity transform of corresponding pixels of image A to image B, and vice-versa. Using the photometric alignment information, along with the spatial alignment information, corresponding pixels of image A and image B may be displayed together in a fused image without a user being able to perceive that a portion of the image was generated from the first camera and a portion of the displayed image was generated by the second camera. Certain aspects of photometric alignment are further described in reference to <FIG>.

The process/system <NUM> also includes fusion <NUM> of a portion of image A and a portion of image B to make a displayable image <NUM> that can be presented to a user to show the target scene being captured by the multi-camera device, where each portion is joined with the other seamlessly such that the displayed image appears to have come from one camera. Fusion of images generated by multiple cameras is further described, for example, in reference to <FIG>.

In some embodiments, in order to accurately perform spatial alignment and intensity equalization, a static calibration operation can be performed on a multi-camera device. <FIG> demonstrates an example of an embodiment of a setup, and stages of, a static calibration procedure according to an embodiment. In some embodiments a multi-camera device <NUM> can two cameras <NUM> and <NUM>. Camera <NUM> can be a wide-angle camera and camera <NUM> can be a telephoto camera. The static calibration operation can be performed at a factory manufacturing the multi-camera device <NUM> where a calibration rig <NUM> can be used. The calibration rig <NUM> can be a planar calibration plate with a checkerboard or dot pattern of known size. The cameras <NUM> and <NUM> can take images of the calibration rig <NUM>. Using the known features and distances on the calibration rig, a transformation <NUM> can be estimated. The transformation <NUM> can include models and parameters of the two asymmetric cameras <NUM> and <NUM>. These parameters can include a scaling factor. The scaling factor can be defined as roughly the ratio of the focal lengths of the two asymmetric cameras <NUM> and <NUM>. The two asymmetric cameras <NUM> and <NUM> have different focal length and magnification, in order to map or juxtapose their images on each other, a scaling factor can be determined. Other parameters of the transformation <NUM> can include a viewpoint matching matrix, principal offset, geometric calibration and other parameters relating the images of the camera <NUM> to the camera <NUM>.

Using the transformation parameters <NUM>, a mapping <NUM> can be generated relating the images from the camera <NUM> to the images from camera <NUM> or vice versa. The mapping <NUM> and transformation parameters <NUM> can be stored in a memory <NUM> of the multi-camera device <NUM>, or a memory component that is not part of the multi-camera device <NUM>. As the multi-camera device <NUM> is subjected to wear and tear and other factors affecting its initial factor calibration, the embodiments described herein can be used to refine, readjust or tune the transformation parameters <NUM> and the mapping <NUM>. For example, the spatial alignment and intensity equalization embodiments described herein can be applied dynamically as the multi-camera device <NUM> is being used by a user to account for shift in transformation parameters <NUM> and mapping <NUM>.

<FIG> is a block diagram illustrating an example of an embodiment of a spatial alignment module <NUM> that can be used to perform spatial alignment of image data generated by two or more cameras that have different imaging characteristics. In one example as described in reference to <FIG>, an image A <NUM> generated by a wide-angle camera can be spatially aligned with an image B generated by a telephoto camera. In other words, spatial alignment is a mapping of pixels in image A <NUM> to align with corresponding pixels in image B <NUM>. The mapping may also be referred to as a transform. As a result of the mapping (or transform), the images from two cameras can be spatially aligned such that when the images are used, in whole or in part (for example, for a fused image that includes a portion of each of image A <NUM> and image B <NUM>), spatially the images appear to be from the same camera (and viewpoint).

In the embodiment illustrated if <FIG>, an image A <NUM> and image B <NUM> are provided to the spatial alignment module <NUM>. In various embodiments, the spatial alignment module <NUM> may be implemented in software, hardware, or a combination of software and hardware. The spatial alignment module <NUM> may use previously determined alignment information (for example, retrieving such information from a memory component). The previously determined alignment information may be used as a starting point for spatial alignment of images provided by the two cameras. The spatial alignment module <NUM> can include a feature detector <NUM> and a feature matcher <NUM>. The feature detector <NUM> may include instructions (or functionality) to detect features (or keypoints) in each of image A <NUM> and image B <NUM> based on criteria that may be predetermined, by one or more of various feature detection techniques known to a person of ordinary skill in the art. The feature matcher <NUM> match the identified features in image A <NUM> to image B <NUM> using a feature matching technique, for example, image correlation. In some embodiments, the images to be aligned may be partitioned into blocks, and feature identification and matching may be performed on a block-to-block level.

The spatial alignment module <NUM> also includes dynamic alignment <NUM>, which can determine spatial transform parameters, for example, scale, rotation, shift, based on feature matching, that can be used to spatially map pixels from image A <NUM> to corresponding pixels in image B <NUM>. In some embodiments the image data A <NUM> can be transformed to be spatially aligned with image data B <NUM>. In other embodiments, the image data B <NUM> can be transformed to be spatially aligned with image data A <NUM>. As a result of feature detection, matching and dynamic alignment, spatial transform (or mapping) information is generated that indicates operations (e.g., scale, rotation, shift) that need to be done to each pixel, or group of pixels, in image A <NUM> to align with a corresponding pixel (or pixels) in image B <NUM>, or vice-versa. Such spatial transform information <NUM> is then stored in a memory component to be later retrieved by a processor (e.g., an image processor) to perform spatial alignment of another image or images from the wide-angle camera or the telephoto camera. In some implementations, transformed image data <NUM> may also be stored in a memory component for later use.

<FIG> illustrates a block diagram of an example of an embodiment of photometric alignment <NUM>. Implementation of photometric alignment can be in software, for example, as a set of instructions in a module stored in memory, or in hardware, or both. Photometric alignment <NUM> may be used to match the color and intensity of pixels in a first image with the corresponding pixels in a second image. Accordingly, this may allow a portion of the first image to be displayed with a portion of the second image in a preview image such that the portions appear to have been generated from the same camera instead of two different cameras with different imaging parameters as such parameters affect intensity and color. In some embodiments, photometric alignment may be performed on two images generated with asymmetric cameras, for example, on images generated from a wide-angel camera and on images generated from a telephoto camera.

In the embodiment illustrated in <FIG>, data representing image A <NUM> is received from a wide-angle camera and data representing image B <NUM> is received from a telephoto camera. In <FIG>, the aligned image A data <NUM> and aligned image B data <NUM> have been spatially aligned such that pixels from one of the images spatially align with corresponding pixels of the other image. In other embodiments, information provided to photometric alignment <NUM> may include predetermined alignment information and/or the unaligned images generated from a first camera and a second camera. In some examples, data representing image A <NUM> can be spatially transformed image data A received from the spatial alignment module <NUM> in <FIG> and data representing image B <NUM> can be spatially transformed image data B received from the spatial alignment module <NUM> (<FIG>). Image A <NUM> and image B <NUM> can have variations in intensity values, for example pixel intensity values at and around keypoint features. Although the depicted embodiment is implemented to equalize the intensity values of two images, three or more images can be sent to the intensity alignment module <NUM> in other embodiments. In some embodiments of intensity alignment between three or more images, one image can be identified as a reference for matching the intensity values of the other images to the intensity values of the reference image. In some embodiments, the first image sensor and the second image sensor are not asymmetric.

In this example, photometric alignment <NUM> includes several functional features or modules, described below. Image A data <NUM> can be received at partition module A 714A to be partitioned into K regions of pixel blocks. Image B data <NUM> can be received at partition module B 714B to be partitioned into the same number K regions of pixel blocks. The number, size, location, and shape of the pixel blocks may be based on identification of keypoints in image A and image B. In some embodiments, the images can be partitioned according to a predetermined block number and configuration.

Partitioned image data A can be received at histogram analysis module A 744A and partitioned image data B can be received at histogram analysis module B 744B. Though depicted as separate modules, in some embodiments histogram analysis module A and histogram analysis module B can be implemented as a single module. The histogram analysis modules A and B 744A, 744B can operate to determine a histogram for each of one or more colors, for example, red, green and blue, although each is not described separately herein. For each block of K blocks in each of images A and B, histogram analysis module A and histogram analysis module B can compute a probability mass function hi as shown below <MAT> for values of i from <NUM> to K and for j=<NUM>,<NUM>. <NUM> which is the number of values for level j divided by the total number of elements per block N. Accordingly, hi is the probability mass function (PMF) of the block. This indicates the likelihood of level j occurring in the block which gives information on the spatial structure content in the region. In other example implementations, other techniques of histogram analysis may be used.

Equalization function H<NUM> can be determined by equalization module A 745A for the histogram output by histogram analysis module A 744A. For example, equalization module A 745A can sum the mass in the PMF according to following equation: <MAT> to compute the cumulative mass function (CMF). Equalization analysis module B 745B can compute a similar function H<NUM> for the histogram output by histogram analysis module B 744B. Each of equalization analysis module A 745A and equalization analysis module B 745B can operate to determine operate as described herein for each of one or more colors, for example, red, green and blue, although each is not described separately herein. The CMF can indicate how the spatial intensity values change within a block, for example due to features in the block.

Intensity matching module <NUM> can perform a spatial mapping between the intensities of image A and image B based on the cumulative mass functions determined by the equalization modules A and B. In some embodiments, the equalization function can be applied according to: <MAT> once the CMFs for all blocks and all sensors have been determined. This can map the intensity values in image B to the intensity values in image A such that image B is transformed to have a histogram closely resembling or matched to a histogram of image A. As a result, the regions may look very similar and can be identified by subsequent processing as corresponding regions in each image even though they were produced with asymmetric sensors. The resulting intensity matched images A and B can be representing according to: <MAT>.

In other example implementations, other techniques of intensity matching may be used, sometimes being referred to as color transforms or intensity transforms. In some embodiments, in order to determine new intensity values for the pixels of image B, the matching module can perform bilinear histogram interpolation. For example, for each pixel, four new luma values can be determined by table lookup from loaded histograms. The new luma value for the target pixel may then be determined by a suitable interpolation technique, for example bilinearly, in order generate an equalized pixel value from neighboring histogram information.

<FIG> is a block diagram illustrating an example of an embodiment of a process flow that illustrates stages for generating a fused image using spatial alignment and photometric alignment to implement a seamless zoom function. The process flow may include using pre-determined calibration parameters that are stored in memory to rectify or otherwise modify an image, and the calibration parameters may be applied at the preview resolution. The process may also include computing the transform, for example, at the processing (or image) resolution. A spatial transform (e.g., shift and scale) may be computed. Also, a photometric transform (e.g., histogram matching) may be computed. Depending on the state (discussed further in reference to <FIG> and <FIG>), the transform may be computed relative to the last applied transform. The transforms (spatial and photometric) may then be applied, at preview resolution, and the resulting image(s) be provided to an output display as a preview image.

During image capture <NUM>, image A <NUM> and image B <NUM> may depict image information captured of a single target scene (for example, the same or a portion of a single target scene taken at the same time, or at a different time), or of at least some overlapping portions of an target scene (taken at the same time or at a different time). Each of image A <NUM> and image B <NUM> includes information associated with the pixels that are included in the image, for example, an intensity or brightness value (or measured signal) of the pixels as sensed by the imaging system. In some embodiments, a pixel in the image data A and/or B may also have other information relating to the pixel associated with it, for example, information relating to color or another image characteristic.

Processes and techniques for photometric alignment <NUM> may be used for color images (for example, with an RGB color space) to match the color spaces of the image A <NUM> and the image B <NUM> by repeating intensity equalization techniques for each color red, green and blue in the RGB color space. The same intensity equalization techniques may be used for color images in other color spaces, for example in the CMYK color space.

Image A <NUM> and image B <NUM> may be generated using asymmetric cameras disposed a distance apart with different illumination, resulting in spatial misalignment and intensity variations at in the images. In some embodiments, image A <NUM> and image B <NUM> may be formed by sensing radiation having different wavelengths (for example, infra-red radiation (IR) and visible light) used be to construct a multispectral image or for other multispectral imaging applications.

At transform processing <NUM>, the captured image A <NUM> and image B <NUM> can be processed using spatial alignment and intensity equalization to enable accurate matching in the image data. In some embodiments region matching can be used. For example, transform processing <NUM> can implement a spatial alignment module <NUM>, which can include feature detection, which can be configured (or can include instructions) to determine distinctive features for keypoints. Distinctive features can be extracted from each of image A <NUM> and image B <NUM>. Such features can correspond to points in the images that can be matched with a high degree of accuracy. For example, distinctive features may be characterized at least partly by the presence of edges or lines, corners, ridges, or blobs differing in properties including but not limited to size, shape, dimension, brightness or color compared to surrounding pixel regions. As discussed above, such keypoint features can be identified by any known feature detection technique, for example, sum of squared differences, Laplacian of Gaussian (LoG), difference of Gaussian (DoG), and determinant of the Hessian (DoH), to name a few.

However, the described embodiments are not limited to image scenes for which distinctive features can be determined. For example, spatial alignment <NUM> can use projection based alignment when key features cannot be detected or there is insufficient information to perform spatial alignment based on key feature image data.

The transform processing stage <NUM> can also implement feature matching which is configured (or includes instructions) to estimate a projective transform function that maps and aligns the image A <NUM> and the image <NUM> such that the two images have equalized views.

Transform processing <NUM> also may include photometric alignment <NUM>, which may partition image A <NUM> and image B <NUM> into regions comprising pixel blocks based on the identified keypoints. The region size may be determined from the optical and imaging parameters of the respective image capturing systems. The block size can be small enough to capture local variations and encompass keypoint features and large enough to sufficiently sample the local intensity probability density functions. In some embodiments, dividing an image into <NUM>, <NUM>, and <NUM> blocks can result in good performance of region matching using the equalized blocks. Some embodiments can adaptively size blocks based on keypoint features, and may achieve performance gains in region matching using the intensity-equalized and adaptively sized blocks. For example, the pixel block location, shape, and size can be determined based on the location, shape, and size of the identified keypoints. In some embodiments such as some stereoscopic alignment applications, pixel block size can be within a range of larger than a disparity value and smaller than a roll-off variation value. Photometric alignment <NUM> may also include determining key regions to determine a correspondence between regions of image A <NUM> and image B <NUM> in order to identify pairs of pixel blocks depicting the same feature.

The photometric alignment module <NUM> can generate an intensity equalization function for a pair of corresponding regions in image A and image B, for example based at least partly on histogram analysis (for example, as described above in <FIG>). In one embodiment, to equalize local intensity, the probability mass function (PMF) of each block can be determined, the mass in the PMF can be summed to compute the cumulative mass function (CMF), and the CMF can be used to map intensity values from pixels in a first image to pixels in a second image. Accordingly, the intensity equalization between corresponding regions accounts for as the intensity structure of keypoints within the regions.

During image fusion <NUM>, all or portions of two (or more) images may be combined or "fused" to form a preview image. Image fusion <NUM> may receive image A and image B, and transform data <NUM> to spatially and photometrically align the images relative to each other. Based on a zoom level received as an input, a preview image may be generated and displayed. In some embodiments where a wide-angle camera and a telephoto camera are used to generate image A and image B, respectively, the preview image may be generated using either all or a portion of aligned images A and B. For example, when a zoomed-out (or nearly so) input is received, a preview image formed only from image A may be displayed. When a fully zoomed-in (or nearly so) input is received, a preview image formed only from image B may be displayed. For other instances, a portion of image A may be used to form a portion of the preview image (for example, an outer portion) and a portion or all of image B may also be used to form a portion of the preview image (for example, a center portion that may be surrounded by the portion of image A). As preview images are provided based on less or greater zoom levels, the portions of images A and B that are used may vary, and one image may be diminished (or faded) when the input indicates a zoom factor in one direction, and the other image may be diminished (or faded) when the input indicates a zoom factor in the other direction. A state machine <NUM> may be used to control the multiple cameras and the amount of each of images A and B that are used as various zoom factors are received as input (e.g., user input). An example of a state machine is described in references to <FIG> and <FIG>.

The state machine module <NUM>, may retrieve transformation parameters generated from spatial alignment and photometric alignment module and use such parameters to fuse images to form a preview image. The state machine <NUM> may also be configured to store or retrieve a last transformation applied to an image (e.g., image A <NUM> and/or image B <NUM>) such that an inverse transformation can be applied depending on a zoom factor input from a user. A region matching module <NUM> may be used in image fusion <NUM>. For example, a spatially and photometrically aligned representation of image A and/or image B can be used to determine corresponding pixels at keypoints between image data A and image data B. Because the features can appear in different locations, orientations, and scale, the intensity-matched region data is used to determine an accurate geometric translation between corresponding keypoints. Such corresponding keypoints can be used as seams in the preview image to join images together to form the preview image.

<FIG> is a diagram illustrating an example of an embodiment of a state machine <NUM>, which can be used to implement a seamless zoom function in a multi-camera device that includes, for example, a wide-angle camera (for example, first camera <NUM> of <FIG>), a telephoto camera (for example, second camera <NUM> of <FIG>) and a preview image screen (for example, display <NUM> of <FIG>). <FIG> is a graph <NUM> illustrating an example of a zoom function (y-axis) versus time (x-axis) in a multi-camera device. The graph <NUM> illustrated in <FIG> corresponds to the state machine described with reference to <FIG>. For example, states <NUM>-<NUM> are illustrated in different portions of the graph along and just above the x-axis in <FIG>, and in the bold numbers at the top of and inside the circles illustrated in <FIG>. F(z) is a function recited in <FIG> and in <FIG>, the function F(z) being illustrated along the y-axis and representative of a zoom level. The amount of zoom (or the zoom level) increasing along the y-axis in a vertical direction relative to the graph <NUM>. As the zoom level F(z) increases along the y-axis, the target scene in a preview image shown on the display appears closer. In this example, the state machine <NUM> can be in states <NUM>-<NUM>.

Referring to <FIG>, in this example, a multi-camera device receives an input indicting a level of "zoom" desired by a user (for example, input by a user), or required by some functionality. The level of zoom level is represented by the function F(z). At state <NUM>, F(z) is less than a first threshold value T1, a threshold level illustrated on the y-axis of graph <NUM> (<FIG>). In state <NUM>, at the relatively small zoom level less than the first threshold value T1, the wide-angle camera is on and the telephoto camera is off.

If a zoom command is received that indicates a zoom level F(z) between the first threshold value T1 and a second threshold value T2 (<FIG>), the state machine transitions to state <NUM>. In state <NUM>, the wide-angle camera is on and images generated by the wide-angle camera are provided to the display. In some instances, the images provide to the display are modified by a spatial and a photometric transformation before they are displayed. In state <NUM>, the telephoto camera is also on. If a zoom command is received that indicates a zoom level F(z) greater a second threshold value T2, the state machine transitions to state <NUM>. In state <NUM>, the wide-angle camera may be turned off, the telephoto camera remaining on. An output of the telephoto camera can be faded into the output of the wide-angle camera to facilitate a transition from the displayed image of the wide-angle camera to the displayed image of the telephoto camera. In some embodiments, in this and other transitions from displaying images from the wide-angle camera to images from the telephoto camera, the wide-angle camera images may be transformed via a spatial transformation and a photometric transformation to match images from the telephoto camera, and when the transition is completed in state <NUM> and the wide-angle camera is tuned off, only images from the telephoto camera are routed to the preview image display, and such images may be displayed without transforming them spatially or photometrically. Similarly, in some examples, in transitions from displaying images from the telephoto camera to images from the wide-angle camera, the telephoto camera images may be transformed via a spatial transformation and a photometric transformation to match images from the wide-angle camera, and when the transition is completed the telephoto camera is tuned off (for example, in state <NUM>), only images from the wide-angle camera are routed to the preview image display, and such images may be displayed without transforming them spatially or photometrically. In some examples, the state machine remains in a state (for example, state <NUM>) if the zoom level is below the second threshold value and a time constant Cnt is less than a time constant threshold TC to help stabilize the system and prevent oscillations between providing images for display from the telephoto camera and the wide-angle camera at small changes in the zoom level near either the first threshold value T1 or the second threshold value T2. In some implementations the time constant TC is predetermined, and in some embodiments the time constant TC is dynamically determined, for example, based on the change rate of the zoom level input. In some embodiments, the time constant threshold TC can be based on the number of frames of images shown on the preview image display.

The first threshold value T1 and the second threshold values T2 may be selected based on a field of view of the wide-angle camera and/or the telephoto camera. For example, the first threshold value T1 may be selected to encompass a range of field of views within the magnification and autofocus range of the wide-angle camera. For zoom function F(z) values between the first threshold value T1 and second threshold value T2, the state machine in state <NUM> the telephoto camera <NUM> is turned on in preparation for using images from it. If the zoom command F(z) increases beyond the second threshold value T2, the state machine transitions into state <NUM> where the wide-angle camera may be turned off and the output of the telephoto camera can be provide to the display as a preview image. The state machine can remain in state <NUM> if the zoom command is greater or equal to the second threshold value T2.

If the zoom command F(z) is between the first threshold value T1 and the second threshold value T2, and the state machine is in state <NUM>, the state machine can transition to state <NUM>, where the wide-angle camera can be turned on in preparation for a zoom command F(z) which can indicate to display images from the wide-angle camera, such that both the wide-angle camera and the telephoto camera are on and the telephoto camera is "active" (providing at least one image as a preview image on the display. If the state machine is in state <NUM> and the zoom command F(z) is between the first threshold value T1 and the second threshold value T2, the state machine can transition to state <NUM> where the wide-angle camera may be turned on in preparation of receiving a zoom command F(z) which that signals a need for the output of the wide-angle camera to be provided to the display as a preview image. However, if the zoom command F(z) is greater or equal than the second threshold value T2, the state machine can transition to state <NUM> where the telephoto camera is on and the wide-angle camera is turned off.

When the state machine is in state <NUM> and the zoom command F(z) is less than a first threshold value T1, the state machine can transition to state <NUM> where the output of telephoto camera is faded out and the output of the wide-angle camera is faded in, and the telephoto camera may be turned off to save power. If the zoom command F(z) is between the first threshold value T1 and the second threshold value T2 and the state machine is in state <NUM>, the state machine can transition to state <NUM> where both the wide-angle camera and the telephoto camera are turned on. If the state machine is in state <NUM> and the zoom command F(z) is less than the first threshold value T1 and a time constant Cnt is less than a time constant threshold TC, the state machine can remain in state <NUM> where the wide-angle camera is on, and the output of the telephoto camera as seen in a preview image on the display is faded out and the output of the wide-angle camera is faded in. The telephoto camera may subsequently be turned off to save power. If the state machine is in state <NUM> and the zoom command F(z) is less than the first threshold value T1 and the time constant Cnt is greater than the time constant threshold TC, the state machine can transition to state <NUM> where the wide-angle camera is on and the telephoto camera is off, and the output of the wide-angle camera can be provided to as a preview image to the display.

<FIG> illustrates an example of an embodiment of a process <NUM> for a seamless zoom function in a multi-camera device to display images that may be generated by a first and a second asymmetric camera. At block <NUM>, a processor retrieves a first image from a memory component, the first image captured by a first camera having field-of-view (FOV). This may be performed, for example, by the image processor <NUM> and memory component <NUM>. In some embodiments, the first camera may be a wide-angle camera.

At block <NUM>, the process <NUM> further retrieves, using the processor, a second image from a memory component, the second image captured by a second camera having a FOV that is smaller than the FOV of the first camera, the first camera and the second camera positioned such that that a target scene in the FOV of the second camera is also in the FOV of the first camera. In some embodiments, the second camera may be a telephoto camera.

At block <NUM>, the process <NUM> determines, using the processor, a spatial transform. The spatial transform includes information to spatially align pixels of the first image and corresponding pixels of the second image. The process <NUM> then saves the spatial transform in the memory component.

At block <NUM>, the process <NUM> determines, by a processor, a photometric transform. The photometric transform includes information of differences in color and intensity between pixels of the first image and corresponding pixels of the second image, and saving the photometric transform in the memory component.

At block <NUM>, the process <NUM> receives input corresponding to a next preview zoom level. The input may be provided by a component on the multi-camera device configured to receive an input from a user and provide a signal representing the user input.

At block <NUM>, the process <NUM> retrieves the spatial transform information and the photometric transform information from memory, and modifies at least one image received from a first and second cameras by the spatial transform information and the photometric transform information.

At block <NUM>, the process <NUM> provides on the display a preview image comprising at least a portion of the at least one modified image and a portion of either the first image or the second image based on the next preview zoom level.

Implementations disclosed herein provide systems, methods and apparatus for local intensity equalization in region matching techniques. One skilled in the art will recognize that these embodiments may be implemented in hardware, software, firmware, or any combination thereof.

In some embodiments, the circuits, processes, and systems discussed above may be utilized in a wireless communication device. The wireless communication device may be a kind of electronic device used to wirelessly communicate with other electronic devices. Examples of wireless communication devices include cellular telephones, smart phones, Personal Digital Assistants (PDAs), e-readers, gaming systems, music players, netbooks, wireless modems, laptop computers, tablet devices, etc..

The wireless communication device may include one or more image sensors, one or more image signal processors, and a memory including instructions or modules for carrying out the local intensity equalization techniques discussed above. The device may also have data, a processor loading instructions and/or data from memory, one or more communication interfaces, one or more input devices, one or more output devices such as a display device and a power source/interface. The wireless communication device may additionally include a transmitter and a receiver. The transmitter and receiver may be jointly referred to as a transceiver. The transceiver may be coupled to one or more antennas for transmitting and/or receiving wireless signals.

The wireless communication device may wirelessly connect to another electronic device (e.g., base station). A wireless communication device may alternatively be referred to as a mobile device, a mobile station, a subscriber station, a user equipment (UE), a remote station, an access terminal, a mobile terminal, a terminal, a user terminal, a subscriber unit, etc. Examples of wireless communication devices include laptop or desktop computers, cellular phones, smart phones, wireless modems, e-readers, tablet devices, gaming systems, etc. Wireless communication devices may operate in accordance with one or more industry standards such as the 3rd Generation Partnership Project (3GPP). Thus, the general term "wireless communication device" may include wireless communication devices described with varying nomenclatures according to industry standards (e.g., access terminal, user equipment (UE), remote terminal, etc.).

The functions described herein may be stored as one or more instructions on a processor-readable or computer-readable medium. The term "computer-readable medium" refers to any available medium that can be accessed by a computer or processor. By way of example, and not limitation, such a medium may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. It should be noted that a computer-readable medium may be tangible and non-transitory. The term "computer-program product" refers to a computing device or processor in combination with code or instructions (e.g., a "program") that may be executed, processed or computed by the computing device or processor. As used herein, the term "code" may refer to software, instructions, code or data that is/are executable by a computing device or processor.

Software or instructions may also be transmitted over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of transmission medium.

In other words, unless a specific order of steps or actions is required for proper operation of the method that is being described, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.

It should be noted that the terms "couple," "coupling," "coupled" or other variations of the word couple as used herein may indicate either an indirect connection or a direct connection. For example, if a first component is "coupled" to a second component, the first component may be either indirectly connected to the second component or directly connected to the second component. As used herein, the term "plurality" denotes two or more. For example, a plurality of components indicates two or more components.

In the foregoing description, specific details are given to provide a thorough understanding of the examples. However, it will be understood by one of ordinary skill in the art that the examples may be practiced without these specific details. For example, electrical components/devices may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, such components, other structures and techniques may be shown in detail to further explain the examples.

Headings are included herein for reference and to aid in locating various sections. These headings are not intended to limit the scope of the concepts described with respect thereto. Such concepts may have applicability throughout the entire specification.

Claim 1:
A multi-camera device (<NUM>), comprising:
a first camera (<NUM>) having field-of-view, FOV, the first camera configured to generate one or more first images;
a second camera (<NUM>) having a FOV that is smaller than the FOV of the first camera, the second camera configured to generate one or more second images, the first camera and the second camera positioned such that that a target scene in the FOV of the second camera is also in the FOV of the first camera;
a display (<NUM>);
a memory component (<NUM>); and
a processor (<NUM>) coupled to the memory component (<NUM>), the processor (<NUM>) configured to:
retrieve a first image from the memory component;
retrieve a second image from the memory component;
determine a spatial transform, the spatial transform including information to spatially align pixels of the first image and corresponding pixels of the second image, and save the spatial transform in the memory component;
determine a photometric transform, the photometric transform including information of differences in color and intensity between pixels of the first image and corresponding pixels of the second image, and save the photometric transform in the memory component;
receive input corresponding to a next preview zoom level;
retrieve information of the spatial transform and the photometric transform from memory;
modify at least one of the retrieved first and second images using the spatial transform information and the photometric transform information based on the next preview zoom level; and
provide on the display a preview image, wherein:
when the next preview zoom level represents a zoom level below a first threshold value T1 the display image comprises an image only from the first camera and the processor (<NUM>) is further configured to reduce power to the second camera (<NUM>);
when the next preview zoom level represents a zoom level above a second threshold value T2 that is greater than the first threshold value T1, the display image comprises an image only from the second camera and the processor (<NUM>) is further configured to reduce power to the first camera (<NUM>); and
characterized in that
otherwise, the display image comprises a portion of the modified image and a portion of an image from the first camera or the second camera, based on the next preview zoom level.