Restoration of Blurred-Noisy Images Through the Concept of Bilevel Programming

Finding a compromise between regularity to remove noise and preserving image fidelity for natural images is unarguably a non-trivial problem. This paper proposes a new image restoration algorithm that executes an optimal tradeoff between sharpness and noise to warrant an acceptable result of image restoration based on bilevel programming. The algorithm demands an objective functions to perform denoising on the degraded image for the lower-level problem using the curvelet-based denoising method, while the upper-level problem with ultimate objective function that is to obtain restored image by performing deblurring to the denoised image using an improved Wiener filter. Experiments were conducted for synthetically blurred and noisy images. The experimental result shows that the algorithm successfully restores image detail. Numerical measurements of the image quality reveal that the algorithm is comparable with other state-of-the-art methods and has the advantage for image contrast and preserving edge details.