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Non-blind deconvolution aims to restore a sharp image from its blurredcounterpart given an obtained kernel. Existing deep neural architectures areoften built based on large datasets of sharp ground truth images and trainedwith supervision. Sharp, high quality ground truth images, however, are notalways available, especially for biomedical applications. This severely hampersthe applicability of current approaches in practice. In this paper, we proposea novel non-blind deconvolution method that leverages the power of deeplearning and classic iterative deconvolution algorithms. Our approach combinesa pre-trained network to extract deep features from the input image withiterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shotoptimisation process is employed to integrate the deconvolved features,resulting in a high-quality reconstructed image. By performing the preliminaryreconstruction with the classic iterative deconvolution method, we caneffectively utilise a smaller network to produce the final image, thusaccelerating the reconstruction whilst reducing the demand for valuablecomputational resources. Our method demonstrates significant improvements invarious real-world applications non-blind deconvolution tasks.

Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration