Microsoft Technology Licensing, LLC

Huawei Technologies Co., Limited

Qualcomm Incorporated

Jiaya Jia

Jian Qiao Sun

Heung-Yeung Shum

Kwok Wai Hung

Li Xu

Xiaopeng Zhang

Liang Shen

Chen Feng

Xiaoyong Shen

Shaojie Zhuo

An image alignment method and apparatus, where the method and apparatus include obtaining image information of two to-be-aligned images, determining, using a cross-correlation measurement model, first coordinate offset according to the image information of the two images, where the first coordinate offset are used to indicate position deviations of to-be-aligned pixels between the two images in the coordinate system, and aligning the two images according to coordinates of pixels in the first image in the coordinate system and the first coordinate offset.

Image alignment method and apparatus

A method for generating a sharp image based on a blurry image is provided. The method includes acquiring pixel values of pixels in the blurry image, and a convolution kernel of the blurry image, determining a deconvolution kernel of the blurry image based on a preset image gradient operator and the convolution kernel, determining pixel values of pixels in the sharp image based on the deconvolution kernel and the pixel values of the pixels in the blurry image, and generating the sharp image based on the pixel values of the pixels in the sharp image. A deconvolution kernel in the embodiments of the present disclosure is determined based on a gradient operator and a convolution kernel, in other words, the deconvolution kernel introduces the gradient operator as a regularization constraint, which prevents noise from affecting an image recovery process, and improves the quality of a recovered sharp image.

Method and apparatus for generating sharp image based on blurry image

Systems and methods for multispectral imaging are disclosed. The multispectral imaging system can include a near infrared (NIR) imaging sensor and a visible imaging sensor. The disclosed systems and methods can be implemented to de-noise a visible light image using a gradient scale map generated from gradient vectors in the visible light image and a NIR image. The gradient scale map may be used to determine the amount of de-noising guidance applied from the NIR image to the visible light image on a pixel-by-pixel basis.

Near infrared guided image denoising

Systems and methods provide drag-and-drop pasting for seamless image composition. In one implementation, a user casually outlines a region of a source image that contains a visual object to be pasted into a target image. An exemplary system automatically calculates a new boundary within this region, such that when pasted at this boundary, visual seams are minimized. The system applies a shortest path calculation to find the optimal pasting boundary. The best path has minimal color variation along its length, thus avoiding structure and visual objects in the target image and providing the best chance for seamlessness. Poisson image editing is applied across this optimized boundary to blend colors. When the visual object being pasted has fine structure at its border that could be truncated by the Poisson editing, the exemplary system integrates the alpha matte of the visual object into the Poisson equations to protect the fine structure.

Drag-and-drop pasting for seamless image composition

An exemplary method includes receiving a trimap for an image that specifies a background region, a foreground region and an unknown region for the image wherein a boundary exists between the foreground region and the unknown region and wherein another boundary exists between the unknown region and the background region, solving a set of Poisson equations having boundary conditions for the two boundaries to provide a matte that distinguishes a foreground region from a background region in the unknown region, and refining the matte by solving a set of Poisson equations for a local unknown region. Various other exemplary technologies are also presented.

Poisson matting for images

Techniques are disclosed to improve quality of images that may be blurred or underexposed (e.g., because of camera shake, taken in dim lighting conditions, or taken of high action scenes). The techniques may be implemented in a digital camera, digital video camera, or a digital camera capable of capturing video. In one described implementation, a digital camera includes an image sensor, a storage device, and a processing unit. The image sensor captures two images from a same scene which are stored on the storage device. The processing unit enhances the captured images with luminance correction.

Digital cameras with luminance correction

Techniques are disclosed to improve quality of images that may be blurred or underexposed. In one described implementation, a method includes providing two images of a same scene. The method determines a spatial coherence and color statistics of the two images. The determined color statistics and spatial coherence are utilized to enhance one of the two images.

Growing community of inventors

Hong Kong, China

Jiaya Jia