A low-complexity method for efficient depth-guided image deblurring
This addresses the need for efficient image deblurring on resource-constrained devices like mobile platforms, though it is incremental as it builds on existing depth-guided methods.
The paper tackles the problem of high computational complexity in deep learning-based image deblurring by introducing a low-complexity neural network that uses depth maps and wavelet transforms, achieving competitive image quality while reducing complexity by up to two orders of magnitude.
Image deblurring is a challenging problem in imaging due to its highly ill-posed nature. Deep learning models have shown great success in tackling this problem but the quest for the best image quality has brought their computational complexity up, making them impractical on anything but powerful servers. Meanwhile, recent works have shown that mobile Lidars can provide complementary information in the form of depth maps that enhance deblurring quality. In this paper, we introduce a novel low-complexity neural network for depth-guided image deblurring. We show that the use of the wavelet transform to separate structural details and reduce spatial redundancy as well as efficient feature conditioning on the depth information are essential ingredients in developing a low-complexity model. Experimental results show competitive image quality against recent state-of-the-art models while reducing complexity by up to two orders of magnitude.