IMAGE ARTIFACT REDUCTION USING FILTER DATA BASED ON DEEP IMAGE PRIOR OPERATIONS
A method includes obtaining first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data. The method also includes performing a plurality of deep image prior operations, using an image prior based on the first image data, to generate filter data. The method further includes modifying the first image data based on the filter data to generate second image data. The method also includes performing an artifact reduction process based on the second image data to generate third image data.
1 . A method comprising:
obtaining, by one or more processors, first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data;
performing, by the one or more processors, a plurality of deep image prior operations, using an image prior based on the first image, to generate filter data;
modifying, by the one or more processors, the first image data based on the filter data to generate second image data; and
performing, by the one or more processors, an artifact reduction process based on the second image data to generate third image data.
2 . The method of claim 1 , wherein the waveform return data represent reflections, from a visually occluded structure, of one or more incident waves, and wherein the third image data represents an enhanced image of the structure as compared to the first image data.
3 . The method of claim 1 , wherein obtaining the first image data includes determining the first image data using a physics-based model.
4 . The method of claim 1 , wherein obtaining the first image data includes performing reverse time migration based on at least a subset of the waveform return data.
5 . The method of claim 1 , wherein the estimated solution to the inverse problem comprises a reflectivity image.
6 . The method of claim 1 , wherein the first image data represents an image and the filter data represents a partial reconstruction of the image generated by early stopping the deep image prior operations such that the plurality of deep image prior operations includes fewer deep image prior operations than would be used to fully reconstruct the image.
7 . The method of claim 1 , wherein determining the filter data based on the first image data includes performing a plurality of iterations of machine-learning optimization, wherein a particular iteration of the machine-learning optimization includes:
providing input data to a model to determine first output data based on values of parameters of the model;
determining an error value based on a comparison of the first output data and the image prior; and
adjusting one or more of the values of the parameters of the model to reduce an error value associated with a subsequent iteration of the machine-learning optimization.
8 . The method of claim 7 , wherein an initial iteration of the machine-learning optimization includes setting the values of the parameters of the model independently of the first image data.
9 . The method of claim 7 , wherein the input data for an initial iteration of the machine-learning optimization is independent of the first image data.
10 . The method of claim 1 , further comprising, before modifying the first image data based on the filter data, performing one or more data transform operations to emphasize artifacts in the filter data.
11 . The method of claim 1 , wherein modifying the first image data based on the filter data includes subtracting the filter data from the first image data.
12 . The method of claim 1 , further comprising, after determining the third image data:
providing the third image data as input to a physics-based model to generate fourth image data; and
performing a second plurality of iterations of the artifact reduction process to generate fifth image data, wherein artifacts are reduced in the fifth image data relative to the fourth image data.
13 . The method of claim 1 , wherein the artifact reduction process includes a plurality of iterations and a particular iteration of the artifact reduction process includes:
determining, using a machine-learning model, a gradient associated with particular solution data; and
adjusting the particular solution data based on the gradient to generate updated solution data.
14 . The method of claim 13 , wherein, during the particular iteration of the artifact reduction process, the particular solution data is adjusted further based on a step size parameter.
15 . The method of claim 14 , further comprising, after performing the plurality of iterations of the artifact reduction process:
adjusting the step size parameter; and
performing a second plurality of iterations of the artifact reduction process.
16 . The method of claim 13 , wherein, during the particular iteration of the artifact reduction process, the particular solution data is adjusted to satisfy a specified constraint.
17 . The method of claim 13 , wherein the machine-learning model corresponds to a score-matching network.
18 . A system comprising:
one or more processors configured to:
obtain first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data;
perform a plurality of deep image prior operations, using an image prior based on the first image data, to generate filter data;
modify the first image data based on the filter data to generate second image data; and
perform an artifact reduction process based on the second image data to generate third image data, wherein artifacts are reduced in the third image data relative to the first image data.
19 . The system of claim 18 , wherein obtaining the first image data includes performing reverse time migration based on at least a subset of the waveform return data.
20 . The system of claim 18 , wherein determining the filter data based on the first image data includes performing a plurality of iterations of machine-learning optimization, wherein a particular iteration of the machine-learning optimization includes:
providing input data to a model to determine first output data based on values of parameters of the model;
determining an error value based on a comparison of the first output data and the image prior; and
adjusting one or more of the values of the parameters of the model to reduce an error value associated with a subsequent iteration of the machine-learning optimization.
21 . The system of claim 20 , wherein an initial iteration of the machine-learning optimization includes setting the values of the parameters of the model independently of the first image data.
22 . The system of claim 21 , wherein the input data for an initial iteration of the machine-learning optimization is independent of the first image data.
23 . The system of claim 18 , wherein modifying the first image data based on the filter data includes subtracting the filter data from the first image data.
24 . A computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to:
obtain first image data that is based on waveform return data and is descriptive of an estimated solution to an inverse problem associated with the waveform return data;
perform a plurality of deep image prior operations, using an image prior based on the first image data, to generate filter data;
modify the first image data based on the filter data to generate second image data; and
perform an artifact reduction process based on the second image data to generate third image data, wherein artifacts are reduced in the third image data relative to the first image data.