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 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: 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 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 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.

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 reference input frame and a non-reference input frame; separating a luma plane for a luma channel and a chroma plane for a chroma channel for each of the reference input frame and the non-reference input frame; generating a luma frame using the luma plane; generating a chroma frame using the chroma plane in a patch-based chroma blending; and combining the luma frame and the chroma frame into an output frame.

Abstract: A method of generating an image from multiple cameras having different focal lengths is described. The method comprising receiving a wide image and a tele image; aligning the wide image and the tele image to overlap a common field of view; correcting for photometric differences between the wide image and the tele image; selecting a stitching seam for the wide image and the tele image; and joining the wide image and the tele image to generate a composite image, wherein a first portion of the composite image on one side of the stitching seam is from the wide image and a second portion of the composite image on the other side of the stitching seam is from the tele image. An electronic device for generating an image is also described.

Abstract: A method of processing image data in a composite image is described. The method comprises analyzing a multi-level blending of images of the composite image; identifying sharp transition boundary areas of the composite image; applying less attenuation for higher levels of multi-level blending of the composite image in the sharp transition boundary areas; and applying more attenuation for lower levels of multi-level blending of the composite image in the sharp transition boundary areas.

Abstract: A method includes obtaining, using at least one image sensor of an electronic device, a first image frame of a scene. The method also includes using a convolutional neural network to generate, from the first image frame, multiple second image frames simulated to have different exposures. One or more objects in the scene in each second image frame are aligned with one or more corresponding objects in the scene in at least one other second image frame and are aligned with one or more corresponding objects in the scene in the first image frame. The method further includes blending the multiple second image frames to generate a final image of the scene.

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, multiple image frames of a scene. The multiple image frames include a plurality of short image frames at a first exposure level and a plurality of long image frames at a second exposure level longer than the first exposure level. The method also includes generating a short reference image frame and a long reference image frame using the multiple image frames. The method further includes selecting, using a processor of the electronic device, the short reference image frame or the long reference image frame as a reference frame, where the selection is based on an amount of saturated motion in the long image frame and an amount of a shadow region in the short image frame. In addition, the method includes generating a final image of the scene using the reference frame.

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.

Abstract: A method to enhance quality of an image comprises receiving the image; identifying a region of the image associated with an object in the image; applying first image processing for the object in the image; applying second image processing on the remaining portions of the image.

Abstract: A method includes capturing multiple ambient images of a scene using at least one camera of an electronic device and without using a flash of the electronic device. The method also includes capturing multiple flash images of the scene using the at least one camera of the electronic device and during firing of a pilot flash sequence using the flash. The method further includes analyzing multiple pairs of images to estimate exposure differences obtained using the flash, where each pair of images includes one of the ambient images and one of the flash images that are both captured using a common camera exposure and where different pairs of images are captured using different camera exposures. In addition, the method includes determining a flash strength for the scene based on the estimate of the exposure differences and firing the flash based on the determined flash strength.