Systems and methods for image processing

1. A method for image processing, implemented on at least one machine each of which has at least one processor and at least one storage device, comprising:

obtaining a preliminary image;

generating an estimated background image by performing an iterative wavelet transformation operation on the preliminary image;

generating an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image, the first objective function being associated with sparsity of the intermediate image, wherein the first objective function includes an L-2 norm of a fourth difference between the estimated background image and a first difference between the intermediate image and the preliminary image; and

generating a target image based on the intermediate image.

2. The method of claim 1 , wherein the obtaining a preliminary image comprises:

performing Wiener inverse filtering on image data generated by an image acquisition device.

3. The method of claim 1 , wherein the first objective function further includes a first weight factor relating to image fidelity of the intermediate image.

4. The method of claim 1 , wherein the iterative wavelet transformation operation includes one or more iterations, and each current iteration comprises:

determining an input image based on the preliminary image or an estimated image generated in a previous iteration;

generating a decomposed image by performing a multilevel wavelet decomposition operation on the input image;

generating a transformed image by performing an inverse wavelet transformation operation on the decomposed image;

generating an updated transformed image based on the transformed image and the input image;

generating an estimated image for the current iteration by performing a cut-off operation on the updated transformed image; and

in response to determining that a termination condition is satisfied, designating the estimated image as the background image.

5. The method of claim 1 , wherein the first objective function is further associated with continuity of the intermediate image, and the first objective function includes a Hessian matrix of the intermediate image.

6. The method of claim 1 , wherein the first objective function further includes a second weight factor relating to the sparsity of the intermediate image, or an L-1 norm of the intermediate image.

7. The method of claim 1 , wherein the first iterative operation comprises:

determining an initial estimated intermediate image based on the preliminary image; and

updating an estimated intermediate image by performing, based on the initial estimated intermediate image, a plurality of iterations of the first objective function, wherein in each of the plurality of iterations,

in response to determining that a termination condition is satisfied,

finalizing the intermediate image.

8. The method of claim 2 , wherein the generating the target image based on the intermediate image comprises:

performing, based on a second objective function, a second iterative operation on the intermediate image, the second objective function including an L-2 norm of a third difference between the intermediate image and the target image multiplied by a system matrix of the image acquisition device.

9. The method of claim 8 , wherein the second iterative operation includes an iterative deconvolution.

10. The method of claim 2 , wherein the image acquisition device includes a structured illumination microscope or a fluorescence microscope.

11. A method for image processing, implemented on at least one machine each of which has at least one processor and at least one storage device, comprising:

generating a preliminary image by filtering image data generated by an image acquisition device;

generating an estimated background image by performing an iterative wavelet transformation operation on the preliminary image;

generating an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image, the first iterative operation including sparse deconvolution of the intermediate image, wherein the first objective function includes an L-2 norm of a fourth difference between the estimated background image and a first difference between the intermediate image and the preliminary image; and

generating a target image by performing, based on a second objective function, a second iterative operation on the intermediate image, the second objective function being associated with a system matrix of the image acquisition device, the intermediate image, and the target image.

12. The method of claim 11 , wherein the filtering the image data comprises:

filtering the image data by performing Wiener inverse filtering on the image data.

13. The method of claim 11 , wherein the first objective function is associated with image fidelity of the intermediate image, continuity of the intermediate image, or sparsity of the intermediate image.

14. The method of claim 11 , further comprising:

generating an estimated background image by performing an iterative wavelet transformation operation on the preliminary image, the first objective function being associated with the estimated background image.

15. The method of claim 14 , wherein the iterative wavelet transformation operation includes one or more iterations, and each current iteration comprises:

determining an input image based on the preliminary image or an estimated image generated in a previous iteration;

generating a decomposed image by performing a multilevel wavelet decomposition operation on the input image;

generating a transformed image by performing an inverse wavelet transformation operation on the decomposed image;

generating an updated transformed image based on the transformed image and the input image;

generating an estimated image for the current iteration by performing a cut-off operation on the updated transformed image; and

in response to determining that a termination condition is satisfied, designating the estimated image as the background image.

16. The method of claim 11 , wherein the first iterative operation comprises:

determining an initial estimated intermediate image based on the preliminary image; and

updating an estimated intermediate image by performing, based on the initial estimated intermediate image, a plurality of iterations of the first objective function, wherein in each of the plurality of iterations,

in response to determining that a termination condition is satisfied, finalizing the intermediate image.

17. The method of claim 11 , wherein the second iterative operation includes an iterative deconvolution, and the second objective function is associated with a third difference between the intermediate image and the target image multiplied by the system matrix.

18. A system for image processing, comprising:

at least one storage medium including a set of instructions; and

at least one processor in communication with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to cause the system to perform operations including:

obtaining a preliminary image;

generating an estimated background image by performing an iterative wavelet transformation operation on the preliminary image;

generating an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image, the first objective function being associated with sparsity of the intermediate image, wherein the first objective function includes an L-2 norm of a fourth difference between the estimated background image and a first difference between the intermediate image and the preliminary image; and

generating a target image based on the intermediate image.