Abstract: An imaging device according to the present disclosure includes an exposure section and a controller. The controller controls, at an exposure timing based on a predicted exposure timing at which a state of a subject shot at a first frame rate is predicted to satisfy a predetermined condition, the exposure section at a second frame rate. The second frame rate is different from the first frame rate.

Abstract: A method for optimization of alignment of pixels of X different types on a sensor array for the performance of N different camera tasks, where X and N are integers greater than 1, includes obtaining an output of each of the X types of pixel on the sensor array for an input image; evaluating the quality of the output for each of the N different camera tasks; and obtaining an optimal pixel alignment pattern by adjustment of a potential pixel alignment pattern with respect to performance of the N different camera tasks subject to a user-determined balance of priorities among the N different camera tasks.

Abstract: An imaging device according to the present disclosure includes an exposure section and a controller. The controller controls, at an exposure timing based on a predicted exposure timing at which a state of a subject shot at a first frame rate is predicted to satisfy a predetermined condition, the exposure section at a second frame rate. The second frame rate is different from the first frame rate.

Abstract: A camera of the present disclosure includes an imaging section, a zooming section, a framing section, and a controller. The controller controls, in a case where one of the zooming section and the framing section is operated by a user operation, another of the zooming section and the framing section that is not operated by the user operation, in accordance with a movement of a subject.

Abstract: Provided are a device and a method that execute movement prediction of a moving subject included in an input image from an imaging unit, generate a future prediction image, and display the image on a display unit. An image processing unit that executes movement prediction of a moving subject included in an input image from an imaging unit, generates a future prediction image, and displays the image on a display unit is provided. The future prediction image to be displayed is an image in which a delay time based on a delay factor that occurs between a subject photographing start preparation operation by a user and completion of image photographing is taken into consideration, and is, for example, a future prediction image ahead by a total delay time of a display delay for the display unit and a delay time between a shutter operation and image recording.

Abstract: Various aspects of a method and a system for image processing are disclosed herein. The method includes processing an input image, which comprises structure information and detail information, using image processing (IP) blocks in an image processing pipeline. One or more of the IP blocks, such as the “lossy” IP blocks, process the input image with at least a partial loss of the detail information. By replacing the “lossy” IP blocks with redesigned image processing (IP) modules, the image processing pipeline reduces or avoids such loss of the detail information. A more efficient implementation of the improved pipeline is realized by using a master IP module when the “lossy” IP blocks are reordered and grouped together in the image processing pipeline. The method is further extended to process 3-D images to reduce or avoid loss of detail information in a 3-D image processing pipeline.

Abstract: Various aspects of a method and device to process one or more multi-channel images are disclosed herein. The method to process one or more multi-channel images is executed within an electronic device. A channel distance value between each channel of a target patch and corresponding channel of one or more neighbor patches of one or more multi-channel images is determined. The determination of the channel distance value is based on channel data in each channel of the one or more multi-channel images. Based on relative signal information in each channel of the target patch, a channel weight for each channel is dynamically determined. Based on the determined channel distance value and the dynamically determined channel weight for each channel, a patch matching score is determined.

Abstract: A method for processing a multi-channel image includes modification of adjusted pixel values of one or more pixels in a region of the multi-channel image, in accordance with a pre-specified signal pattern based on one or more parameters. A score is determined based on a ratio of a pixel difference value and a maximum pixel step value. The maximum pixel step value corresponds to modified pixel values of the one or more pixels in the region when the maximum pixel step value exceeds a threshold noise level for the region. False color is suppressed in a selected region of the multi-channel image based on the determined score when the false color is identified in the region.

Abstract: An image processing system, and a method of operation thereof, includes: an imaging sensor and lens assembly for receiving a first shot; and a defocus blur unit for calculating a defocus blur based on the first shot; wherein: the imaging sensor is for receiving a second shot with the defocus blur; and further comprising: a low-pass filter for generating a low-pass image by filtering the first shot based on the defocus blur; a comparison unit for comparing the low-pass image with the second shot for detecting a color aliasing; and a correction unit for correcting the color aliasing in the first shot.

Abstract: An image processing system, and a method of operation thereof, includes: an edge motion generation unit for detecting edges in a first image frame and a second image frame stored by a storage device or a memory and for generating edge motion vectors between the first image frame and the second image frame based on the edges; a motion vector list generation unit for extracting dominant motion vectors from a group of the edge motion vectors and for generating a motion vector list based on the dominant motion vectors; an image segmentation unit for generating a segmentation of the first image frame; an initial motion generation unit for generating initial motion vectors based on the segmentation and the motion vector list; and a smooth motion generation unit for generating smooth motion vectors based on the initial motion vectors and for generating a dense optical flow field by combining the smooth motion vectors.

Abstract: A method for processing a multi-channel image includes modification of adjusted pixel values of one or more pixels in a region of the multi-channel image, in accordance with a pre-specified signal pattern based on one or more parameters. A score is determined based on a ratio of a pixel difference value and a maximum pixel step value. The maximum pixel step value corresponds to modified pixel values of the one or more pixels in the region when the maximum pixel step value exceeds a threshold noise level for the region. False color is suppressed in a selected region of the multi-channel image based on the determined score when the false color is identified in the region.

Abstract: An image processing system, and a method of operation thereof, includes: an imaging sensor and lens assembly for receiving a first shot; and a defocus blur unit for calculating a defocus blur based on the first shot; wherein: the imaging sensor is for receiving a second shot with the defocus blur; and further comprising: a low-pass filter for generating a low-pass image by filtering the first shot based on the defocus blur; a comparison unit for comparing the low-pass image with the second shot for detecting a color aliasing; and a correction unit for correcting the color aliasing in the first shot.

Abstract: An image processing system, and a method of operation thereof, includes: an edge motion generation unit for detecting edges in a first image frame and a second image frame stored by a storage device or a memory and for generating edge motion vectors between the first image frame and the second image frame based on the edges; a motion vector list generation unit for extracting dominant motion vectors from a group of the edge motion vectors and for generating a motion vector list based on the dominant motion vectors; an image segmentation unit for generating a segmentation of the first image frame; an initial motion generation unit for generating initial motion vectors based on the segmentation and the motion vector list; and a smooth motion generation unit for generating smooth motion vectors based on the initial motion vectors and for generating a dense optical flow field by combining the smooth motion vectors.

Abstract: An image processing system, and a method of operation thereof, includes: a local patch ternarization module for receiving an input image, for calculating a mean value of a local patch of pixels in the input image, and for calculating ternary values for the pixels based on the mean value; and an artifact removal module, coupled to the local patch ternarization module, for removing a residue artifact based on the ternary values and for generating an output image with the residue artifact removed for sending to an image signal processing hardware.

Abstract: Various aspects of a method and device to process one or more multi-channel images are disclosed herein. The method to process one or more multi-channel images is executed within an electronic device. A channel distance value between each channel of a target patch and corresponding channel of one or more neighbor patches of one or more multi-channel images is determined. The determination of the channel distance value is based on channel data in each channel of the one or more multi-channel images. Based on relative signal information in each channel of the target patch, a channel weight for each channel is dynamically determined. Based on the determined channel distance value and the dynamically determined channel weight for each channel, a patch matching score is determined.

Abstract: A method to improve video quality by suppressing noise and artifacts in difference frames of a video is described herein.

Abstract: A method to improve video quality by suppressing noise and artifacts in difference frames of a video is described herein.

Abstract: Various aspects of a method and a system for image processing are disclosed herein. The method includes processing an input image, which comprises structure information and detail information, using image processing (IP) blocks in an image processing pipeline. One or more of the IP blocks, such as the “lossy” IP blocks, process the input image with at least a partial loss of the detail information. By replacing the “lossy” IP blocks with redesigned image processing (IP) modules, the image processing pipeline reduces or avoids such loss of the detail information. A more efficient implementation of the improved pipeline is realized by using a master IP module when the “lossy” IP blocks are reordered and grouped together in the image processing pipeline. The method is further extended to process 3-D images to reduce or avoid loss of detail information in a 3-D image processing pipeline.

Abstract: An image processing system, and a method of operation thereof, includes: a local patch ternarization module for receiving an input image, for calculating a mean value of a local patch of pixels in the input image, and for calculating ternary values for the pixels based on the mean value; and an artifact removal module, coupled to the local patch ternarization module, for removing a residue artifact based on the ternary values and for generating an output image with the residue artifact removed for sending to an image signal processing hardware.

Abstract: An image processing device includes: a peripheral objective pixel random acquiring section acquiring pixels obtained by sampling peripheral pixels located in a periphery of a marked pixel which is marked at random with respect to the marked pixel of pixels corresponding to an objective image as an object of noise removal as peripheral objective pixels as pixels used for filtering for noise removal; and a noise removing section carrying out filtering for the noise removal by using the peripheral objective pixels.