Abstract: A method includes obtaining, using at least one processor, an image of a scene including image data in each of multiple channels of a color space. The method also includes decomposing a channel of the image into a first base layer and a first detail layer. The method further includes generating a first enhanced detail layer by manipulating a characteristic of the first detail layer. The method also includes decomposing the first base layer into a second base layer and a second detail layer. The method further includes generating a second enhanced detail layer by manipulating a characteristic of the second detail layer. In addition, the method includes combining the first enhanced detail layer, the second enhanced detail layer, and the second base layer to obtain an enhanced image of the scene.

Abstract: An electronic device includes at least one imaging sensor and at least one processor coupled to the at least one imaging sensor. The at least one imaging sensor is configured to capture a burst of image frames. The at least one processor is configured to generate a low-resolution image from the burst of image frames. The at least one processor is also configured to estimate a blur kernel based on the burst of image frames. The at least one processor is further configured to perform deconvolution on the low-resolution image using the blur kernel to generate a deconvolved image. In addition, the at least one processor is configured to generate a high-resolution image using super resolution (SR) on the deconvolved image.

Abstract: A method includes receiving a selection of a selected zoom area on an input image frame displayed on a user interface; determining one or more candidate zoom previews proximate to the selected zoom area using a saliency detecting algorithm; and displaying the one or more candidate zoom previews on the user interface adjacent to the selected zoom area.

Abstract: A method includes capturing a plurality of first images using a first image sensor and a plurality of second images using a second image sensor. The method also includes generating a short single view, short depth map, long single view, and long depth map from the first and second images. The method further includes generating a multi-frame depth map using the short single view, short depth map, long single view, and long depth map. The method also includes generating a gain map using the multi-frame depth map and lighting conditions, where the lighting conditions introduce a relighting effect into the gain map. The method further includes generating a relit short single view and a relit long single view by applying the gain map to the short and long single views. In addition, the method includes generating a relit image by fusing the relit short and long single views.

Abstract: A method includes capturing a plurality of first images using a first image sensor and a plurality of second images using a second image sensor. The method also includes generating a short single view, short depth map, long single view, and long depth map from the first and second images. The method further includes generating a multi-frame depth map using the short single view, short depth map, long single view, and long depth map. The method also includes generating a gain map using the multi-frame depth map and lighting conditions, where the lighting conditions introduce a relighting effect into the gain map. The method further includes generating a relit short single view and a relit long single view by applying the gain map to the short and long single views. In addition, the method includes generating a relit image by fusing the relit short and long single views.

Abstract: A method includes obtaining, using at least one processor, an image of a scene including image data in each of multiple channels of a color space. The method also includes decomposing a channel of the image into a first base layer and a first detail layer. The method further includes generating a first enhanced detail layer by manipulating a characteristic of the first detail layer. The method also includes decomposing the first base layer into a second base layer and a second detail layer. The method further includes generating a second enhanced detail layer by manipulating a characteristic of the second detail layer. In addition, the method includes combining the first enhanced detail layer, the second enhanced detail layer, and the second base layer to obtain an enhanced image of the scene.

Abstract: A method includes obtaining, using at least one processor of an electronic device, a high dynamic range (HDR) input Bayer image. The method also includes generating, using the at least one processor of the electronic device, a plurality of synthesized images at different exposure levels based on the input Bayer image. The method further includes fusing, using the at least one processor of the electronic device, the synthesized images to generate a fused image. The method also includes generating, using the at least one processor of the electronic device, a gain map based on the fused image. In addition, the method includes applying, using the at least one processor of the electronic device, a gain based on the gain map to the input Bayer image.

Abstract: A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Abstract: A method includes obtaining, using at least one processor of an electronic device, multiple calibration parameters associated with multiple sensors of a selected mobile device. The method also includes obtaining, using the at least one processor, an identification of multiple imaging tasks. The method further includes obtaining, using the at least one processor, multiple synthetically-generated scene images. In addition, the method includes generating, using the at least one processor, multiple training images and corresponding meta information based on the calibration parameters, the identification of the imaging tasks, and the scene images. The training images and corresponding meta information are generated concurrently, different ones of the training images correspond to different ones of the sensors, and different pieces of the meta information correspond to different ones of the imaging tasks.

Abstract: A method includes obtaining, using at least one sensor of an electronic device, multiple image frames of a scene. The multiple image frames include a first image frame and a second image frame captured using different exposures. The method also includes excluding, using at least one processor of the electronic device, pixels in the first and second image frames based on a coarse motion map. The method further includes generating, using the at least one processor, multiple local histogram match maps based on different portions of the first and second image frames. In addition, the method includes generating, using the at least one processor, an image of the scene using the local histogram match maps.

Abstract: A method includes receiving a reference image and a non-reference image; dividing the reference image into a plurality of tiles; determining, using an electronic device, a motion vector map using coarse-to-fine based motion vector estimation; and generating an output frame using the motion vector map with the reference image and the non-reference image.

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 encoding, using at least one processor, each of the first image frame and the second image frames using a convolutional neural network to generate a corresponding feature map. The method further includes aligning, using the at least one processor, encoded features of the feature map corresponding to the first image frame with encoded features of the feature maps corresponding to the second image frames.

Abstract: An electronic device, method, and computer readable medium for compositing high dynamic range frames are provided. The electronic device includes a camera, and a processor coupled to the camera. The processor registers the plurality of multi-exposure frames with a hybrid of matched features to align non-reference frames with a reference frame; generates blending maps of the plurality of multi-exposure frames to reduce moving ghost artifacts and identify local areas that are well-exposed in the plurality of multi-exposure frames; and blends the plurality of multi-exposure frames weighted by the blending maps using a two-step weight-constrained exposure fusion technique into a high dynamic range (HDR) frame.

Abstract: A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.

Abstract: A method includes obtaining multiple image frames of a scene using at least one camera of an electronic device and processing the multiple image frames to generate a higher-resolution image of the scene. Processing the multiple image frames includes generating an initial estimate of the scene based on the multiple image frames. Processing the multiple image frames also includes, in each of multiple iterations, (i) generating a current estimate of the scene based on the image frames and a prior estimate of the scene and (ii) regularizing the generated current estimate of the scene. The regularized current estimate of the scene from one iteration represents the prior estimate of the scene in a subsequent iteration. The iterations continue until the estimates of the scene converge on the higher-resolution image of the scene.

Abstract: A method includes obtaining multiple image frames of a scene using at least one camera of an electronic device. The method also includes using a convolutional neural network to generate blending maps associated with the image frames. The blending maps contain or are based on both a measure of motion in the image frames and a measure of how well exposed different portions of the image frames are. The method further includes generating a final image of the scene using at least some of the image frames and at least some of the blending maps. The final image of the scene may be generated by blending the at least some of the image frames using the at least some of the blending maps, and the final image of the scene may include image details that are lost in at least one of the image frames due to over-exposure or under-exposure.

Abstract: A method includes obtaining multiple image frames of a scene using at least one sensor of an electronic device. The multiple image frames include a first image frame and a second image frame having a longer exposure than the first image frame. The method also includes generating a label map that identifies pixels in the multiple image frames that are to be used in an image. The method further includes generating the image of the scene using the pixels extracted from the image frames based on the label map. The label map may include multiple labels, and each label may be associated with at least one corresponding pixel and may include a discrete value that identifies one of the multiple image frames from which the at least one corresponding pixel is extracted.

Abstract: A method includes receiving a selection of a selected zoom area on an input image frame displayed on a user interface; determining one or more candidate zoom previews proximate to the selected zoom area using a saliency detecting algorithm; and displaying the one or more candidate zoom previews on the user interface adjacent to the selected zoom area.

Abstract: A method includes obtaining multiple image frames of a scene using at least one sensor of an electronic device. The multiple image frames include a first image frame and a second image frame having a longer exposure than the first image frame. The method also includes generating a label map that identifies pixels in the multiple image frames that are to be used in an image. The method further includes generating the image of the scene using the pixels extracted from the image frames based on the label map. The label map may include multiple labels, and each label may be associated with at least one corresponding pixel and may include a discrete value that identifies one of the multiple image frames from which the at least one corresponding pixel is extracted.

Abstract: A method for multi-frame blending includes obtaining at least two image frames of a scene. One of the image frames is associated with a shorter exposure time and a higher sensitivity and representing a reference image frame. At least one other of the image frames is associated with a longer exposure time and a lower sensitivity and representing at least one non-reference image frame. The method also includes blending the reference and non-reference image frames into a blended image such that (i) one or more motion regions of the blended image are based more on the reference image frame and (ii) one or more stationary regions of the blended image are based more on the at least one non-reference image frame.