D3: Deep Deconvolution Deblurring for Natural Images

In this paper, we propose to reformulate the blind image deblurring task to directly learn an inverse of the degradation model represented by a deep linear network. We introduce Deep Identity Learning (DIL), a novel learning strategy that includes a dedicated regularization term based on the propert...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Saraswathula, Vamsidhar, Gorthi, Rama Krishna
Format: Article
Language:English
Subjects:
Datasets
Deep learning
Degradation
Fourier transforms
Image resolution
Image restoration
Linear systems
Real time
Regularization
Representations
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Summary:In this paper, we propose to reformulate the blind image deblurring task to directly learn an inverse of the degradation model represented by a deep linear network. We introduce Deep Identity Learning (DIL), a novel learning strategy that includes a dedicated regularization term based on the properties of linear systems, to exploit the identity relation between the degradation and inverse degradation models. The salient aspect of our proposed framework is it neither relies on a deblurring dataset nor a single input blurry image (e.g. Polyblur, a self-supervised method). This framework detours the typical degradation kernel estimation step involved in most of the existing blind deblurring solutions by the proposition of our Random Kernel Gallery (RKG) dataset. The proposed approach extends our previous Image Super-Resolution (ISR) work, NSSR-DIL, to the image deblurring task. In this work, we updated the regularization term in DIL based on Fourier transform properties of the identity relation, to deliver robust performance across a wide range of degradations. Besides the regularization term, we provide an explicit and compact representation of the learned deep linear network in a matrix form, called Deep Restoration Kernel (DRK) to perform image restoration. Our experiments show that the proposed method outperforms both traditional and deep learning based deblurring methods, with at least an order of 100 lesser computational resources. The D3 model, both LCNN & DRK, can be effortlessly extended to the Image Super-Resolution (ISR) task as well to restore the low-resolution images with fine details. The D3 model and its kernel form representation (DRK) are lightweight yet robust and restore the blurry input in a fraction of a second.
ISSN:2331-8422