Signal enhancement and manipulation using a signal-specific deep network
AssigneeYeda Research and Development Co. Ltd.
InventorsMichal Irani, Assaf Shocher
Life of this patent
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Abstract
Given an input image, an image enhancement task, and no external examples available to train on, an Image-Specific Deep Network is constructed tailored to solve the task for this specific image. Since there are no external examples available to train on, the network is trained on examples extracted directly from the input image itself. The current solution solves the problem of Super-Resolution (SR), whereas the framework is more general and is not restricted to SR.
Claims18
A computer implemented method for performing a task on a specific single test signal;
- the task is at least one selected from:
- super-resolution, deblurring, denoising, completion of missing data, distortion correction, correcting compression artifacts, dehazing, signal-enhancement, signal-manipulation, and degradation removal;
- the method comprising:
- constructing a signal-specific deep learning neural network (DLNN), by selecting number of nodes, number of layers, and their inter connections, configured for said task and said specific single test signal;
- generating training target outputs from said specific single test signal, via transformations thereof;
- generating a corresponding training input for each of said training target outputs, by degrading said training target outputs;
- training said DLNN to perform said task, using a training set comprising pairs of said training target outputs and their said corresponding training inputs, whereby the DLNN is trained to perform said task only on said specific single test signal and signals originated therefrom;
- and
- applying said trained DLNN on said specific single test signal, wherein said specific single test signal is provided as an input to said trained DLNN, to obtain an output signal in accordance with said task.
A computer implemented method for increasing a resolution of a specific single test image, the method comprising:
- constructing an image-specific deep learning neural network (DLNN), by selecting number of nodes, number of layers, and their inter connections, configured specifically for increasing the resolution of said specific single test image;
- generating training target outputs from said specific single test image, via transformations thereof;
- generating a corresponding training input for each of said training target outputs, by downscaling resolution of said training target outputs;
- training said DLNN to increase image resolution, using a training set comprising pairs of said training target outputs and their said corresponding training inputs, whereby the DLNN is trained to increase resolution only of said specific single test image and images originated therefrom;
- and
- applying said trained DLNN on said specific single test image, wherein said specific single test image is provided as an input to said trained DLNN, to obtain an output image with increased-resolution thereof.
Dependencies are inferred from the claim text.