Abstract: A method includes obtaining multiple input image frames and generating bad pixel maps associated with different ones of the input image frames. Each bad pixel map is generated using first and second adaptive thresholds that are based on statistical properties of image data contained in a specified operation window within the associated input image frame. The method also includes generating at least one refined bad pixel map using the bad pixel maps associated with two or more of the input image frames. The method further includes using one or more coordinates of one or more bad pixels identified in the at least one refined bad pixel map to update one or more pixel values of at least one of the input image frames.

Abstract: A method includes obtaining an input image that contains blur. The method also includes providing the input image to a trained machine learning model, where the trained machine learning model includes (i) a shallow feature extractor configured to extract one or more feature maps from the input image and (ii) a deep feature extractor configured to extract deep features from the one or more feature maps. The method further includes using the trained machine learning model to generate a sharpened output image. The trained machine learning model is trained using ground truth training images and input training images, where the input training images include versions of the ground truth training images with blur created using demosaic and noise filtering operations.

Abstract: A method includes obtaining, using at least one under display camera, one or more first image frames associated with a first diffraction pattern and one or more second image frames associated with a second diffraction pattern. The first diffraction pattern and the second diffraction pattern are related through a transformation. The method also includes generating a first deblurred image using the one or more first image frames and a second deblurred image using the one or more second image frames. The method further includes combining the first and second deblurred images while exploiting complementary types of image artifacts created by the first and second diffraction patterns to generate an image of a scene.

Abstract: 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.

Abstract: A method includes obtaining, using at least one processing device of an electronic device, multiple image frames having different exposure levels. The method also includes determining whether a moving saturated region is present in at least some of the image frames. The method further includes selecting one of the image frames as a reference frame, where the selected image frame depends on whether the moving saturated region is present in at least some of the image frames. The selected image frame may have a first exposure level when the moving saturated region is present in at least some of the image frames, and the selected image frame may have a second exposure level longer than the first exposure level when the moving saturated region is not present in the image frames.

Abstract: 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.

Abstract: 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.

Abstract: A method includes obtaining a ground truth image and generating multiple image frames using the ground truth image, a modeled optical blur, and a modeled global motion. The method also includes generating multiple mosaic image frames using the image frames and a color filter array and generating multiple raw input image frames using the mosaic image frames and a noise model associated with at least one imaging sensor. The method further includes providing the raw input image frames to a multi-frame processing pipeline in order to generate synthetic training data. In addition, the method includes training a machine learning-based image processing engine using the ground truth image and the synthetic training data.

Abstract: A method includes obtaining, using at least one processing device of an electronic device, an input image containing blur. The method also includes generating, using the at least one processing device, an edge enhancement mask and a gain mask based on the input image. The method further includes generating, using the at least one processing device, a halo-suppressed edge mask based on the edge enhancement mask and the gain mask. In addition, the method includes generating, using the at least one processing device, a sharpened image based on the input image and the halo-suppressed edge mask.

Abstract: 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.

Abstract: 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.

Abstract: A method includes obtaining, using at least one processing device of an electronic device, raw image frames of a scene. The raw image frames include different sets of raw image frames captured at different viewpoints and different viewing angles relative to the scene. The method also includes performing, using the at least one processing device, blending of each set of raw image frames in order to generate blended image frames of the scene. The method further includes training, using the at least one processing device, a machine learning model using the blended image frames. The machine learning model is trained to generate three-dimensional (3D) information about the scene from viewpoints and viewing angles not captured in the sets of raw image frames.

Abstract: An apparatus includes at least one processing device configured to obtain input frames from a video. The at least one processing device is also configured to generate a forward flow from a first input frame to a second input frame and a backward flow from the second input frame to the first input frame. The at least one processing device is further configured to generate an occlusion map at an interpolated frame coordinate using the forward flow and the backward flow. The at least one processing device is also configured to generate a consistency map at the interpolated frame coordinate using the forward flow and the backward flow. In addition, the at least one processing device is configured to perform blending using the occlusion map and the consistency map to generate an interpolated frame at the interpolated frame coordinate.

Abstract: A method includes obtaining multiple video frames. The method also includes determining whether a bi-directional optical flow between the multiple video frames satisfies an image quality criterion for bi-directional consistency. The method further includes identifying a non-linear curve based on pixel coordinate values from at least two of the video frames. The at least two video frames include first and second video frames. The method also includes generating interpolated video frames between the first and second video frames by applying non-linear interpolation based on the non-linear curve. In addition, the method includes outputting the interpolated video frames for presentation.

Abstract: A method includes obtaining multiple training image frames of a scene, where the training image frames are captured at multiple viewpoints and multiple viewing angles relative to the scene. The method also includes generating multiple initial motion maps using the training image frames and identifying three-dimensional (3D) feature points associated with the scene using the training image frames. The method further includes generating tuned motion masks using the initial motion maps and projections of the 3D feature points onto the initial motion maps. In addition, the method includes training a machine learning model using the training image frames and the tuned motion masks, where the machine learning model is trained to generate 3D information about the scene from viewpoints and viewing angles not captured in the training image frames.

Abstract: 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.

Abstract: A method includes capturing a plurality of first images using a first image sensor and capturing a plurality of second images using a second image sensor. The method also includes estimating depth information based on at least one of the first images and at least one of the second images. The method further includes obtaining information related to a lighting direction from an artificial intelligence (AI) light director, where the AI light director is trained to determine one or more lighting directions. In addition, the method includes generating at least one relit image using at least one of the first and second images based on the obtained information related to the lighting direction and the estimated depth information.

Abstract: 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.

Abstract: An apparatus includes at least one processing device configured to obtain an input image and determine a cumulative distribution function (CDF) histogram from a luminance or luma (Y) channel of the input image. The at least one processing device is also configured to determine an entry CDF histogram in a CDF histogram lookup table (LUT) closest to the determined CDF histogram. The at least one processing device is further configured to apply a Y channel global tone mapping (GTM) curve to the input image based on one or more parameters assigned to the entry CDF histogram from the CDF histogram LUT.

Abstract: A method includes obtaining, using a stationary sensor of an electronic device, multiple image frames including first and second image frames. The method also includes generating, using multiple previously generated motion vectors, a first motion-distorted image frame using the first image frame and a second motion-distorted image frame using the second image frame. The method further includes adding noise to the motion-distorted image frames to generate first and second noisy motion-distorted image frames. The method also includes performing (i) a first multi-frame processing (MFP) operation to generate a ground truth image using the motion-distorted image frames and (ii) a second MFP operation to generate an input image using the noisy motion-distorted image frames.