Noise-adaptive non-blind image deblurring

1. A method of performing noise-adaptive non-blind deblurring on an input image that includes blur and noise, the method comprising:

implementing, using processing circuitry, a first neural network on the input image to obtain one or more parameters, wherein the implementing the first neural network includes obtaining a one-dimensional vector of singular values from the input image and implementing a one-dimensional residual convolutional neural network (CNN);

performing, using the processing circuitry, regularized deconvolution to obtain a deblurred image from the input image, wherein the regularized deconvolution uses the one or more parameters to control noise in the deblurred image; and

implementing, using the processing circuitry, a second neural network to remove artifacts from the deblurred image and provide an output image.

2. The method according to claim 1 , wherein the implementing the first neural network results in one parameter that is a regularization parameter.

3. The method according to claim 1 , wherein the implementing the first neural network results in two or more parameters that are weights corresponding with a set of predefined regularization parameters.

4. The method according to claim 1 , further comprising training the first neural network and the second neural network individually or together in an end-to-end arrangement.

5. The method according to claim 1 , further comprising obtaining, by the processing circuitry, a point spread function that defines the blur in the input image.

6. The method according to claim 5 , wherein the input image is obtained by a camera in a vehicle and the point spread function is obtained from one or more sensors of the vehicle or from the camera based on a calibration.

7. A non-transitory computer-readable storage medium storing instructions which, when processed by processing circuitry, cause the processing circuitry to implement a method of performing noise-adaptive non-blind deblurring on an input image that includes blur and noise, the method comprising:

implementing a first neural network on the input image to obtain one or more parameters, wherein the implementing the first neural network includes obtaining a one-dimensional vector of singular values from the input image and implementing a one-dimensional residual convolutional neural network (CNN);

performing regularized deconvolution to obtain a deblurred image from the input image, wherein the regularized deconvolution uses the one or more parameters to control noise in the deblurred image; and

implementing a second neural network to remove artifacts from the deblurred image and provide an output image.

8. The non-transitory computer-readable storage medium according to claim 7 , wherein the implementing the first neural network results in one parameter that is a regularization parameter.

9. The non-transitory computer-readable storage medium according to claim 7 , wherein the implementing the first neural network results in two or more parameters that are weights corresponding with a set of predefined regularization parameters.

10. The non-transitory computer-readable storage medium according to claim 7 , further comprising training the first neural network and the second neural network individually or together in an end-to-end arrangement.

11. The non-transitory computer-readable storage medium according to claim 7 , further comprising obtaining, by the processing circuitry, a point spread function that defines the blur in the input image.

12. The non-transitory computer-readable storage medium according to claim 11 , wherein the input image is obtained by a camera in a vehicle and the point spread function is obtained from one or more sensors of the vehicle or from the camera based on a calibration.

13. A vehicle comprising:

a camera configured to obtain an input image that includes blur and noise; and

processing circuitry configured to implement a first neural network on the input image to obtain one or more parameters, wherein the first neural network is configured to obtain a one-dimensional vector of singular values from the input image and implement a one-dimensional residual convolutional neural network (CNN), to perform regularized deconvolution to obtain a deblurred image from the input image, wherein the regularized deconvolution uses the one or more parameters to control noise in the deblurred image, and to implement a second neural network to remove artifacts from the deblurred image and provide an output image.

14. The vehicle according to claim 13 , wherein the processing circuitry is configured to implement the first neural network and obtain one parameter that is a regularization parameter or obtain two or more parameters that are weights corresponding with a set of predefined regularization parameters.

15. The vehicle according to claim 13 , wherein the processing circuitry is configured to train the first neural network and the second neural network individually or together in an end-to-end arrangement.

16. The vehicle according to claim 13 , wherein the processing circuitry is configured to obtain a point spread function that defines the blur in the input image.

17. The vehicle according to claim 16 , wherein the processing circuitry is configured to obtain the point spread function from one or more sensors of the vehicle that measure a movement of the vehicle or from a calibration of the camera.