Abstract: Implementations generally relate to optimizing a photo album layout. In some implementations, a method includes receiving a plurality of images and determining a target arrangement. The method also includes arranging the plurality of images in an N-dimensional arrangement based on a predetermined distance function. The method also includes arranging the plurality of images in the target arrangement based on the N-dimensional arrangement.

Abstract: Implementations generally relate to optimizing a photo album layout. In some implementations, a method includes receiving a plurality of images and determining a target arrangement. The method also includes arranging the plurality of images in an N-dimensional arrangement based on a predetermined distance function. The method also includes arranging the plurality of images in the target arrangement based on the N-dimensional arrangement.

Abstract: Implementations relate to estimating noise in images using a polynomial relationship for pixel values of image features. In some implementations, a computer-implemented method to estimate noise in an image includes determining a plurality of patches of pixels in the image. For each patch of pixels, the method determines feature pixels in the patch that are included in a particular image feature at least partially depicted in the patch. The method determines an error estimate for each patch of pixels, where each error estimate is based on an amount by which pixel values of the feature pixels in the patch of pixels are different from an estimated polynomial relationship between the feature pixels in the patch of pixels. One of the error estimates is selected as a noise level estimate for the image.

Abstract: Implementations relate to detecting and modifying facial features of persons in images. In some implementations, a method includes receiving one or more general color models of color distribution for a facial feature of persons depicted in training images. The method obtains an input image, and determines a feature mask associated with the facial feature for one or more faces in the input image. Determining the mask includes estimating one or more local color models for each of the faces in the input image based on the general color models, and iteratively refining the estimated local color models based on the general color models. The refined local color models are used in the determination of the feature mask. The method applies a modification to the facial feature of faces in the input image using the feature mask.

Abstract: Implementations generally relate to color corrections to photos. In some implementations, a method includes determining a face profile from a plurality of photos of a face. The method further includes receiving a target photo of the face. The method further includes matching the face in the target photo with the face in the plurality of photos. The method further includes performing one or more color adjustments to the face in the target photo based on the face profile.

Abstract: Implementations relate to adjusting images using a texture mask. In some implementations, a method includes detecting one or more texture regions having detected texture in an image, and generating a mask from the image based on the detected texture regions. The detected texture regions are distinguished in the mask from other regions of the image that do not have detected texture. The method applies one or more adjustment operations to the image in amounts based on values of the mask.