Image registration is a crucial part of the success of the super-resolution algorithms. In real applications, atmospheric turbulence is an important factor that brings further degradation to the low-resolution image sequence (video frames), besides other degradations such as global motion due to movement of the optical device, blurring due to the point spread function of the lens, and blurring due to the finite size of the detector array. In this paper, the degradation of the atmospheric turbulence to the low-resolution images is modeled as per-pixel motion in the high-resolution plane and is assumed to be spatially local and temporally quasi-periodic. The registration is a two-stage process: first, the global motion between frames is estimated using the phase-correlation method to remove "jitter" and stabilize the sequence; then, an optical flow method with quasi-periodic constraint is used to estimate the per-pixel motion. A threshold is used to separate the relatively larger object movement from per-pixel atmospheric turbulence. After registration, the shift map of each frame is obtained, along with a prototype of the high-resolution image. A maximum a posteriori (MAP) based super-resolution algorithm is therefore applied to reconstruct the high-resolution image. Experiments using synthetic images are conducted to verify the validity of the proposed method. Finally, conclusions are drawn.
We propose a technique for the estimation of the regularization parameter for image resolution enhancement (superresolution) based on the assumptions that it should be a function of the regularized noise power of the data and that its choice should yield a convex functional whose minimization would give the desired high-resolution image. The regularization parameter acts adaptively to determine the trade-off between fidelity to the received data and prior information about the image. Experimental results are presented and conclusions are drawn.
In this paper, we extend our previous resolution enhancement results by proposing a technique for the estimation of the regularization parameter based on the assumption that it should satisfy the following properties: It should be a function of the regularized noise power of the data and its choice should yield a convex functional whose minimization would give the desired high-resolution image. Experimental results are presented and conclusions are drawn.