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

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

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

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

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

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

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.