Hand-held photography in low-light conditions presents a number of challenges to capture high quality images. Capturing using a high ISO results in noisy images, while capturing using longer exposure results in blurry images. This necessitates post-processing techniques to restore the latent image. Most existing methods try to estimate the latent image either by denoising or by deblurring a single image. Both these approaches are ill-posed and often result in unsatisfactory results. A few methods try to alleviate this ill-posedness using a pair of noisy-blurry images as inputs. However, most of the methods using this approach are computationally very expensive. In this paper, we propose a fast method to estimate a latent image given a pair of noisy-blurry images. To accomplish this, we propose a deep-learning based approach that uses scale space representation of the images. To improve computational efficiency, we process higher scale spaces using shallower networks and the lowest scale using a deeper network. Also, unlike existing scale-space methods that use bi-cubic interpolation, we propose a content preserving scale space transformation for decimation and interpolation. The proposed method generates state-of-the-art results at reduced computational complexity compared to state-of-the-art method. Finally, we also show that computational efficiency can be improved by 90% compared to baseline with only a marginal drop in PSNR.

https://ieeexplore.ieee.org/document/9746495