A motion detecting section detects a change in relative position relation between a subject and an image capturing section performing a rolling shutter operation. A thinning-out setting section sets a thinning-out amount of a line thinning-out operation of the image capturing section according to the detection result obtained by the motion detecting section. A recognition processing section performs subject recognition in an image obtained by the image capturing section, by using a recognizer corresponding to the thinning-out amount set by the thinning-out setting section. The change in relative position relation is detected based on motion of a moving body on which the image capturing section is mounted, an image capturing scene, an image obtained by the image capturing section, and the like. Line thinning-out is performed during the rolling shutter operation, and the thinning-out amount is set according to the detection result obtained by the motion detecting section.

Searching for documents includes retrieving objects from a physical media image using a camera from a smartphone, a user selecting a subset of the objects, forming a search query based on the subset of objects, and applying the search query to a search engine to search for the documents. Retrieving objects from a media image may include waiting for a view of the camera to stabilize. Waiting for the view of the camera to stabilize may include detecting changing content of a video flow provided to the camera and/or using motion sensors of the camera to detect movement. Retrieving objects may include the smartphone identifying possible subsets of objects in the media image. The user selecting a subset of the objects may include the smartphone presenting at least some of the possible subsets to the user and the user selecting one of the possible subsets.

Depth information can be used to assist with image processing functionality, such as image stabilization and blur reduction. In at least some embodiments, depth information obtained from stereo imaging or distance sensing, for example, can be used to determine a foreground object and background object(s) for an image or frame of video. The foreground object then can be located in later frames of video or subsequent images. Small offsets of the foreground object can be determined, and the offset accounted for by adjusting the subsequent frames or images. Such an approach provides image stabilization for at least a foreground object, while providing simplified processing and reduce power consumption. Similarly processes can be used to reduce blur for an identified foreground object in a series of images, where the blur of the identified object is analyzed.

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

A digital graduated filter is implemented in an imaging device by combining multiple images of the subject wherein the combining may include combining different numbers of images for highlights and for shadows of the subject. The imaging device may present a user with a set of pre-defined graduated filter configurations to choose from. A user may also specify the direction of graduation and strength of graduation in a viewfinder. In an alternative implementation, combining may include scaling of pixels being added instead of varying the number of images being combined. In an alternative implementation, the combining of multiple images may include combining a different number of images for highlights of the subject than for shadows of subject.

This application discloses a photographing method and apparatus, to overcome blurring that occurs during photographing. When a zoom ratio is greater than a first zoom ratio threshold, a long-focus camera is started to capture an image. A zoom ratio of the long-focus camera is greater than or equal to the first zoom ratio threshold. Because a high zoom ratio causes large shake, a rotational blur occurs in the image. According to the photographing method disclosed in this application, a first neural network model for rotational image deblurring is used to implement rotational image deblurring processing. In this way, high imaging quality of an image, a video, or a preview image is presented to a user to some extent, and the imaging effect may not be inferior to the effect of photographing with a tripod.

A blur correction device includes a processor and a memory that is built into or coupled to the processor. The processor is configured to acquire an amount of blur correction used to correct blurring of an image obtained by imaging of an imaging element during exposure for one frame in the imaging element, and correct the blurring by performing image processing based on a most recently acquired amount of blur correction, on an unfinished image that is the image less than one frame that is being read from the imaging element. In a case in which a first reading period does not overlap with a second reading period, the processor corrects the blurring by performing the image processing based on the amount of blur correction acquired during exposure between the first reading period and the second reading period, on the unfinished image of the subsequent frame.

An electronic device is disclosed, including a housing, an optical image stabilization (OIS) coil and an auto-focus (AF) coil, a first lens disposed along an optical axis, a second lens disposed under the first lens, and a third lens disposed under the second lens, the second lens is deformable according to movement of an AF carrier, an OIS carrier including an OIS magnet disposed corresponding to the OIS coil of the housing, and configured to move the third lens along a direction perpendicular to the optical axis, wherein the AF carrier includes an AF magnet corresponding to the AF coil, to move the second lens along the optical axis, and a processor configured to apply a current to the OIS coil to move the OIS carrier, resulting in movement of the third lens in the direction perpendicular to the optical axis, and apply a current to the AF coil to deform the second lens by movement of the AF carrier.

A method of de-smearing an image includes capturing image data from an imaging sensor and collecting motion data indicative of motion of the sensor while capturing the image data. The motion data is collected at a higher frequency than an exposure frequency at which the image data is captured. The method includes modeling motion of the sensor based on the motion data, wherein motion is modeled at the higher frequency than the exposure frequency. The method also includes modeling optical blur for the image data, modeling noise for the image data, and forming a de-smeared image as a function of the modeled motion, the modeled blur, and the modeled noise, and the image data captured from the imaging sensor.

Embodiments provide a lens moving apparatus including a bobbin having a lens barrel, a housing configured to accommodate the bobbin, an upper elastic member coupled to the bobbin and the housing, a lower elastic member coupled to the bobbin and the housing, a first coil disposed on the bobbin, a first magnet disposed on the housing, a circuit board disposed below the housing, a second coil disposed on the circuit board, a first sensor to output a first output signal based on a sensed result of a magnetic field strength of the first magnet, a first capacitor connected in parallel to the first sensor.