Samsung Electronics Co., Ltd.

Wenbo Li

Hongxia Jin

Burak Uzkent

Dawei Li

Qian Lou

Jing Zhu

Yi Wei

Zhipeng Mo

A method includes obtaining raw image data, where the raw image data includes data values each having most significant bits and least significant bits. The method also includes providing the raw image data to a trained machine learning model and generating processed image data using the trained machine learning model. The method further includes presenting an image based on the processed image data. The trained machine learning model is trained to modulate a feature map associated with the most significant bits of the data values of the raw image data based on the least significant bits of the data values of the raw image data in order to generate a fusion of the most significant bits and the least significant bits of the data values of the raw image data.

Fusion techniques for combining most significant bits and least significant bits of image data in image processing or other applications

An inpainting method includes obtaining an image including an object having a delicate shape and identifying a target region within the image, where the target region is adjacent to the object. The method also includes using a first mask to separate the image into a number of semantic categories and aggregating neighboring contexts for the target region based on the semantic categories. The method further includes restoring, based on the aggregated contexts, textures in the target region without affecting the delicate shape of the object. In addition, the method includes displaying a refined image including the restored textures in the target region and the object.

System and method for image inpainting based on layout-guided pre-processing attention mechanism

A super resolution is produced using multiple reference images. Reference images are upsampled and blurred as needed for comparison between images of different resolution. Patches in blurred images are searched to find those patches which can be assembled into vectors for improving feature content over multiple resolution levels. The searches are based on similarity maps. The assembled vectors are concatenated with one or more other vectors, up-converted and then passed through convolutional layers to obtain new feature vectors. A final feature vector is passed through a convolutional layer to obtain the super resolution image.

Image super-resolution with reference images from one or more cameras

An inpainting method includes obtaining image information at an electronic device, where the image information identifies an area corresponding to a removed object within an image. The method also includes reconstructing the area corresponding to the removed object by (i) applying a semantic mask and a surface normal map to identify and rank neighboring contexts of the area and (ii) sampling, using an attention mechanism, the ranked contexts to generate pixel information for the area. The method further includes rendering the image with the reconstructed area.

System and method for indoor image inpainting under multimodal structural guidance

An inpainting method includes retrieving image information at an electronic device, where the image information identifies an area within an image. The method also includes retrieving, using the electronic device, semantic information including a plurality of semantic classes and a semantic class distribution for each semantic class of the plurality of semantic classes. The method further includes generating semantic codes associated with different portions of the image based on the image information and the semantic information. In addition, the method includes constructing the area within the image by generating image content based on the semantic information.

System and method for precise image inpainting to remove unwanted content from digital images

A method includes training, using at least one processor, a specialized teacher model to perform visual object instance segmentation in order to segment and classify objects in first training images. The first training images contain foreground objects without backgrounds. The method also includes training, using the at least one processor, a student model to perform visual object instance segmentation in order to segment and classify objects in second training images. The second training images contain the foreground objects and the backgrounds. Training the student model includes using selected outputs of the specialized teacher model. The method further includes deploying the trained student model to perform visual object instance segmentation in an external device.

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Wenbo Li