Abstract: An image-processing apparatus and method for reducing or removing staircase artifacts comprises an image sensor and circuitry. The image sensor is configured to capture an input image signal. The circuitry is configured to identify a first portion of the input image signal that exhibits a staircase artifact. The circuitry is further configured to generate a slope signal based on a steepness parameter of a slope that corresponds to the first region-of-interest based on the identification. The circuitry is configured to generate a difference signal based on a difference between the first portion of the input image signal and the generated slope signal. The circuitry is configured to generate a de-noised image signal based on the generated difference signal and the generated slope signal to reduce the staircase artifact in the first portion of the input image signal.

Abstract: An image-processing apparatus and method for reducing or removing staircase artifacts comprises an image sensor and circuitry. The image sensor is configured to capture an input image signal. The circuitry is configured to identify a first portion of the input image signal that exhibits a staircase artifact. The circuitry is further configured to generate a slope signal based on a steepness parameter of a slope that corresponds to the first region-of-interest based on the identification. The circuitry is configured to generate a difference signal based on a difference between the first portion of the input image signal and the generated slope signal. The circuitry is configured to generate a de-noised image signal based on the generated difference signal and the generated slope signal to reduce the staircase artifact in the first portion of the input image signal.

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 efficient patch-based method for video denoising is described herein. A noisy patch is selected from a current frame and a motion-compensated patch in the previous denoised frame is found. The two patches go through a patch-denoising process to generate a denoised patch. A rotational buffer and aggregation technique is used to generate the denoised current video frame.

Abstract: An efficient patch-based method for video denoising is described herein. A noisy patch is selected from a current frame and a motion-compensated patch in the previous denoised frame is found. The two patches go through a patch-denoising process to generate a denoised patch. A rotational buffer and aggregation technique is used to generate the denoised current video frame.

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: A method for effectively performing local image similarity measurement is proposed. A system equipped with such a method for effectively performing an image processing task includes an image processor that performs an intermediate-results calculation procedure to calculate intermediate result values that are based upon corresponding pixels of a target patch and one or more similar patches. The image processor typically moves the target patch of the intermediate-results calculation to different locations in a raster order or some other organized order. The image processor then performs an intermediate-results combination procedure by calculating appropriate statistics of the intermediate result values to produce processed pixel values. A processor device typically controls the image processor to effectively perform the image processing tasks including, but not limited to, demosaicing and denoising.

Abstract: A method to select an appropriate window size for local image processing uses two window sizes: a very large but impractical size and a small and practical size. Two images are generated for the desired image processing under both window sizes. The difference of these two images eliminates common non-linear nature of the local image processing and only contains low-frequency artifacts generated under a smaller window size. By taking the Fourier transform of the difference image, an approximated noise spectrum of low-frequency artifacts introduced by the small and practical window size is able to be observed. Alternatively, a frequency analysis of the smaller window size is able to be conducted by calculating the difference of cross-correlation functions and noise spectra under the same two window sizes. An appropriate window size is able to be selected based on such frequency analysis.