The HIO algorithm iteratively refines an initial guess of the object being studied. It alternates between the real and reciprocal space, applying constraints in each domain to improve the accuracy of the reconstruction. The basic steps include:
Starting with an initial guess. Switching to reciprocal space using a Fourier transform. Applying constraints in reciprocal space based on experimental data. Returning to real space via an inverse Fourier transform. Applying real-space constraints, such as known boundaries or support. Repeating the process until convergence is achieved.
