Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.

Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.

Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.

Abstract: In embodiments of statistics of nearest neighbor fields, matching patches of a nearest neighbor field can be determined at image grid locations of a first digital image and a second digital image. A motion field can then be determined based on motion data of the matching patches. Predominant motion components of the motion field can be determined based on statistics of the motion data to generate a final motion field. The predominant motion components correspond to a motion of objects as represented by a displacement between the first and second digital images. One of the predominant motion components can then be assigned to each of the matching patches to optimize the final motion field of the matching patches.

Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.

Abstract: In embodiments of optical flow with nearest neighbor field fusion, an initial motion field can be generated based on the apparent motion of objects between digital images, and the initial motion field accounts for small displacements of the object motion. Matching patches of a nearest neighbor field can also be determined for the digital images, where patches of an initial size are compared to determine the matching patches, and the nearest neighbor field accounts for large displacements of the object motion. Additionally, region patch matches can be compared and determined between the digital images, where the region patches are larger than the initial size matching patches. Optimal pixel assignments can then be determined for a fused image representation of the digital images, where the optimal pixel assignments are determined from the initial motion field, the matching patches, and the region patch matches.

Abstract: In embodiments of optical flow accounting for image haze, digital images may include objects that are at least partially obscured by a haze that is visible in the digital images, and an estimate of light that is contributed by the haze in the digital images can be determined. The haze can be cleared from the digital images based on the estimate of the light that is contributed by the haze, and clearer digital images can be generated. An optical flow between the clearer digital images can then be computed, and the clearer digital images refined based on the optical flow to further clear the haze from the images in an iterative process to improve visibility of the objects in the digital images.

Abstract: In embodiments of spatially coherent nearest neighbor fields, initial matching patches of a nearest neighbor field can be determined at image grid locations of a first digital image and a second digital image. Spatial coherency can be enforced for each matching patch in the second digital image with reference to respective matching patches in the first digital image based on motion data of neighboring matching patches. A multi-resolution iterative process can then update each spatially coherent matching patch based on overlapping grid regions of the matching patches that are evaluated for matching regions of the first and second digital images. An optimal, spatially coherent matching patch can be selected for each of the image grid locations of the first and second digital images based on iterative interaction to enforce the spatial coherency of each matching patch and the multi-resolution iterative process to update each spatially coherent matching patch.

Abstract: In embodiments of optical flow with nearest neighbor field fusion, an initial motion field can be generated based on the apparent motion of objects between digital images, and the initial motion field accounts for small displacements of the object motion. Matching patches of a nearest neighbor field can also be determined for the digital images, where patches of an initial size are compared to determine the matching patches, and the nearest neighbor field accounts for large displacements of the object motion. Additionally, region patch matches can be compared and determined between the digital images, where the region patches are larger than the initial size matching patches. Optimal pixel assignments can then be determined for a fused image representation of the digital images, where the optimal pixel assignments are determined from the initial motion field, the matching patches, and the region patch matches.

Abstract: In embodiments of spatially coherent nearest neighbor fields, initial matching patches of a nearest neighbor field can be determined at image grid locations of a first digital image and a second digital image. Spatial coherency can be enforced for each matching patch in the second digital image with reference to respective matching patches in the first digital image based on motion data of neighboring matching patches. A multi-resolution iterative process can then update each spatially coherent matching patch based on overlapping grid regions of the matching patches that are evaluated for matching regions of the first and second digital images. An optimal, spatially coherent matching patch can be selected for each of the image grid locations of the first and second digital images based on iterative interaction to enforce the spatial coherency of each matching patch and the multi-resolution iterative process to update each spatially coherent matching patch.

Abstract: In embodiments of statistics of nearest neighbor fields, matching patches of a nearest neighbor field can be determined at image grid locations of a first digital image and a second digital image. A motion field can then be determined based on motion data of the matching patches. Predominant motion components of the motion field can be determined based on statistics of the motion data to generate a final motion field. The predominant motion components correspond to a motion of objects as represented by a displacement between the first and second digital images. One of the predominant motion components can then be assigned to each of the matching patches to optimize the final motion field of the matching patches.

Abstract: In embodiments of optical flow accounting for image haze, digital images may include objects that are at least partially obscured by a haze that is visible in the digital images, and an estimate of light that is contributed by the haze in the digital images can be determined The haze can be cleared from the digital images based on the estimate of the light that is contributed by the haze, and clearer digital images can be generated. An optical flow between the clearer digital images can then be computed, and the clearer digital images refined based on the optical flow to further clear the haze from the images in an iterative process to improve visibility of the objects in the digital images.