Samsung Electronics Co., Ltd.

Texas Instruments Corporation

Other

John W Glotzbach

Hamid Rahim Sheikh

Ruiwen Zhen

Ibrahim Pekkucuksen

Long N Le

Zeeshan Nadir

Zhen Tong

Nguyen Thang Long Le

Yibo Xu

Raja Bala

Osman Gokhan Sezer

Anqi Yang

Tyler Luu

Baekdu Choi

William M Johnson

Michael Oliver Polley

Jeffrey L Nye

John Seokjun Lee

Shalini Gupta

David Harold Bartley

Murali S Chinnakonda

Ajay Jayaraj

Toshio Nagata

Gunawath Dilshan Godaliyadda

Stephen Busch

Devendra Kumar Jangid

Rahul Rithe

Robert Jp Nychka

Abhiram Gnanasambandam

Yuting Hu

Ganesh Sundararajan

Klaus Illgner

Omar A Elgendy

Mingzhao Yu

Ibrahim E Pekkucuksen

A method includes obtaining an image and a gain map associated with the image. The method also includes identifying image patches in the image and corresponding gain map patches in the gain map. Different image patches are centered around different anchor points in the image. The method further includes, for each image patch and its corresponding gain map patch, generating an intensity-gain curve for the associated anchor point. The intensity-gain curve specifies (i) gain values based on the corresponding gain map patch for intensity values up to a threshold intensity value and (ii) gain values based on one or more input parameters for intensity values above the threshold intensity value. In addition, the method includes combining the intensity-gain curves to generate a 3D lookup table, which identifies the gain values for the anchor points in the image at each of multiple intensity values.

Generation of three-dimensional (3D) lookup table for tone mapping or other image processing functions

A system and method are provided for noise reduction improvement for blending blurred frames in a multi-frame system. The method includes retrieving a number of frames of an image. The method also includes identifying one or more edges within the frames and comparing an edge strength of a reference frame and an edge strength of a non-reference frame. The method further includes determining a weight reduction factor based on a result of the comparison and applying the weight reduction factor to a blending of multiple frames of the number of frames of the image. In addition, the method includes displaying the blended frames.

System and method for noise reduction for blending blurred frames in a multi-frame system

A method includes obtaining a reference image frame and a non-reference image frame and generating a mask based on the reference image frame and the non-reference image frame. The method also includes determining a scaling coefficient and an offset coefficient for each of multiple color channels of the non-reference image frame based on pixels in the reference image frame and pixels in the non-reference image frame that are identified by the mask. The method further includes applying the scaling and offset coefficients to the non-reference image frame in order to generate a calibration-corrected non-reference image frame.

Dynamic calibration correction in multi-frame, multi-exposure capture

A method includes extracting multiple shallow features from a low-resolution image using a shallow feature extractor that includes a quaternion convolutional network. The method also includes extracting multiple deep features from the multiple shallow features using a deep feature extractor that includes multiple quaternion residual distillation blocks (QRDBs), where each QRDB includes a quaternion self-attention module. The method further includes reconstructing the multiple deep features into a high-resolution image. Each QRDB may further include a quaternion gated deconvolutional feed forward network (QGDFN) configured to suppress one or more of the multiple deep features.

System and method for single image super- resolution for smart device camera

A method includes dividing an image into overlapping image patches each having a specified size. The method also includes analyzing content of each image patch using a mathematical transform technique to classify each image patch into at least one class. The method further includes filtering each image patch for noise suppression by suppressing one or more transform coefficients of the image patch. An amount of suppression for each of the one or more transform coefficients is selected according to the at least one class of the image patch. In addition, the method includes reconstructing the filtered image patches into an output image.

System and method for adaptive discrete cosine transform (DCT) noise filtering for digital images

A method includes obtaining a raw image in a first image domain. The method also includes determining a color distribution and an amount of variation in the raw image. The method further includes using an iterative process to generate a dead leaf image from a blank image in the first image domain. The iterative process includes adding multiple circles and multiple sticks to the blank image until the dead leaf image is filled. The iterative process also includes blurring portions of the dead leaf image during at least one iteration of the iterative process. Textures of the multiple circles are blended based on the color distribution and the amount of variation in the raw image.

System and method for synthetic data generation using dead leaves images

A method includes filtering an input image through a discrete cosine transform based noise filter (DCT-NF). The DCT-NF converts the input image from an original space into a perceptual space, applying gamma correction. The DCT-NF separates luminance channels of the input image from chroma channels of the input image. The DCT-NF divides the input image into overlapping patches and computes DCT transform of the patches. Each patch is a different partial portion of the input image. The DCT-NF suppresses patches that include an input DCT coefficient within a threshold range. The DCT-NF applies an inverse discrete cosine transform (IDCT) to the suppressed patches and remaining overlapping patches that include an input DCT coefficient outside the threshold range. The DCT-NF re-combines luminance and chroma channels of the IDCT-transformed patches. The DCT-NF generates a DCT noise-filtered output image by re-converting the IDCT-transformed patches to the original space by applying inverse gamma correction.

Learning based discrete cosine noise filter

A method includes obtaining a blended red-green-blue (RGB) image frame of a scene. The method also includes performing, using at least one processing device of an electronic device, an interband denoising operation to remove at least one of noise and one or more artifacts from the blended RGB image frame in order to produce a denoised RGB image frame. Performing the interband denoising operation includes performing filtering of red, green, and blue color channels of the blended RGB image frame to remove at least one of the noise and the one or more artifacts from the blended RGB image frame. The filtering of the red and blue color channels of the blended RGB image frame is based on image data of at least one of the green color channel and a white color channel of the blended RGB image frame.

Apparatus and method for interband denoising and sharpening of images

An apparatus includes a memory configured to store images and a processor. The processor is configured to receive an input image. The processor is further configured to partition a human mask in the input image using a segmentation algorithm. The processor is also configured to generate a skin map based on identifying skin in the input image using the human mask. In addition, the processor is configured to process an output image with brightening applied using the skin map.

Automatic face and human subject enhancement algorithm for digital images

A method includes obtaining multiple image frames. The method also includes selecting, using at least one processing device of an electronic device, an asymmetrical image pair from the multiple image frames. The asymmetrical image pair includes a first image frame and a second image frame, where the first image frame has a shorter exposure than the second image frame. The method further includes identifying, using the at least one processing device, one or more features based on the asymmetrical image pair. The method also includes determining, using the at least one processing device, whether the first image frame contains flicker based on the one or more features. In addition, the method includes enabling or disabling, using the at least one processing device, the first image frame as a reference candidate based on the determination whether the first image frame contains flicker.

System and method for flicker avoidance in multi-frame multi-exposure image captures

A method includes obtaining, using at least one image sensor of an electronic device, a first image frame and multiple second image frames of a scene. Each of the second image frames has an exposure time different from an exposure time of the first image frame. The method also includes generating, using at least one processor, blur kernels indicating a motion direction of the first image frame using an optical flow network. The method further includes refining, using the at least one processor, the blur kernels using a convolutional neural network. In addition, the method includes generating, using the at least one processor, a target image frame of the scene using the refined blur kernels and occlusion masks for the second image frames.

System and method for motion warping using multi-exposure frames

A method includes obtaining multiple input image frames and determining how to warp at least one of the input image frames. The method also includes performing an intraband demosaic-warp operation to reconstruct image data in different color channels of the input image frames and warp the at least one input image frame to produce RGB input image frames. The method further includes blending the RGB input image frames to produce a blended RGB image frame, performing an interband denoising operation to produce a denoised RGB image frame, and performing an interband sharpening operation to produce a sharpened RGB image frame. In addition, the method includes performing an interband demosaic operation to substantially equalize high-frequency content in different color channels of the sharpened RGB image frame to produce an equalized sharpened RGB image frame and generating a final image of the scene based on the equalized sharpened RGB image frame.

Apparatus and method for combined intraband and interband multi-frame demosaicing

An electronic device includes a processor configured to estimate green values at red and blue pixel locations of an input Bayer frame based on green values at green pixel locations of the input Bayer frame. The processor is also configured to generate red, green, and blue channels of joint demosaiced-warped output RGB pixels from the input Bayer frame based on the green values at the green pixel locations, the estimated green values at the red and blue pixel locations, an alignment vector map, and kernels for red, green, and blue pixels.

Adaptive sub-pixel spatial temporal interpolation for color filter array

A method includes obtaining multiple images of a scene using at least one red-green-blue-white (RGBW) image sensor. The method also includes generating multi-channel frames at different exposure levels from the images. The method further includes estimating motion across exposure differences between the different exposure levels using a white channel of the multi-channel frames as a guidance signal to generate multiple motion maps. The method also includes estimating saturation across the exposure differences between the different exposure levels to generate multiple saturation maps. The method further includes using the generated motion maps and saturation maps to recover saturations from the different exposure levels and generate a saturation-free RGBW frame. In addition, the method includes processing the saturation-free RGBW frame to generate a final image of the scene.

System and method for multi-exposure, multi-frame blending of red-green-blue-white (RGBW) images

A method for dynamic calibration correction in multi-frame, multi-exposure capture in a camera system includes receiving an amplified non-reference frame (e.g., with non-standard exposure value EV-4) to which a digital gain was applied and a reference frame (e.g., with standard exposure value EV0). The method includes generating a mask based on the reference frame and the amplified non-reference frame. The method includes estimating a scaling coefficient and an offset coefficient for each of a number of channels in the amplified non-reference frame, based on selected pixel locations in the generated mask. The method includes correcting a calibration of the amplified non-reference frame by applying the scaling coefficients for each of the channels. The method also includes outputting a calibration-corrected amplified non-reference frame to a multi-frame processor.

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Allen, TX, United States of America

John W Glotzbach