Blind Iterative Deconvolution Algorithm

In the given context, there exists a method known as the Blind Iterative Deconvolution Algorithm, which can be employed for image restoration without requiring prior knowledge of the Point Spread Function (PSF). This algorithm typically utilizes an iterative optimization approach, where alternating updates between image estimation and PSF refinement are performed during each iteration cycle.

The algorithm executes deconvolution operations based on image characteristics and patterns through an iterative process, achieving image restoration. Key implementation aspects often include Fourier domain transformations for computational efficiency, regularization techniques to handle noise amplification, and convergence criteria to terminate iterations. This method proves particularly valuable as it enables automatic image recovery without prior PSF information, significantly reducing user workload and time investment. Common algorithmic implementations may involve Richardson-Lucy deconvolution variants or maximum likelihood estimation approaches with entropy constraints.