Here Global B.v.

Krishna Kumar Balakrishnan

Anish Mittal

David Johnston Lawlor

Zhanwei Chen

Abinaya Manimaran

An approach is provided for generating a super-resolution image as a higher resolution version of an input image. The approach, for example, involves determining a set of tasks to be performed on the input image to facilitate generating the super-resolution image. The approach also involves selecting a combination of loss functions, wherein each loss function of the combination of loss functions is respectively a task-specific neural network pre-trained to perform a corresponding one of the set of tasks. The approach also involves training the super resolution neural network using the combination of loss functions as one or more layers of the super resolution neural network. The approach also involves using the trained super resolution neural network to generate the super-resolution image as a higher resolution version of the input image.

Method, apparatus, and system for task driven approaches to super resolution

An approach is provided for determining a feature correspondence based on camera geometry. The approach, for example, involves determining a first labeled or detected pixel location in a first image and a second labeled or detected pixel location in a second image. The approach also involves computing a first ray from a first camera position of the first image through the first labeled or detected pixel location. The approach further involves computing a second ray from a second camera position of the second image through the second labeled or detected pixel location. The approach further involves computing a closeness value of the first ray and the second ray. The approach further involves providing an output indicating the feature correspondence between the first labeled or detected pixel location and the second labeled or detected pixel location based on determining that the closeness value is within a threshold value.

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Chicago, IL, United States of America

Krishna Kumar Balakrishnan