Summary of D3: Deep Deconvolution Deblurring For Natural Images, by Vamsidhar Saraswathula and Rama Krishna Gorthi (indian Institute Of Technology (iit) Tirupati et al.
D3: Deep Deconvolution Deblurring for Natural Images
by Vamsidhar Saraswathula, Rama Krishna Gorthi
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to blind image deblurring, dubbed Deep Identity Learning (DIL). Unlike traditional methods, DIL directly learns an inverse of the degradation model represented by a deep linear network. The proposed framework, which includes a dedicated regularization term based on linear systems properties, exploits the identity relation between the degradation and inverse degradation models. The approach is self-supervised, not relying on a deblurring dataset or single input blurry image. Instead, it utilizes the Random Kernel Gallery (RKG) dataset, extending previous work on Image Super-Resolution (ISR). The regularization term is updated based on Fourier transform properties to deliver robust performance across various degradations. The learned deep linear network is represented in a matrix form, called Deep Restoration Kernel (DRK), for image restoration. Experimental results show that the proposed method outperforms traditional and deep learning-based deblurring methods, with reduced computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making blurry images clear again. The authors created a new way to do this called Deep Identity Learning (DIL). Unlike other methods, DIL doesn’t need a special dataset or image to learn from. Instead, it uses a unique approach that helps it understand how blurry images work. This means the method is more efficient and can restore images quickly. The results show that DIL outperforms other methods and can even be used for another task called Image Super-Resolution. |
Keywords
» Artificial intelligence » Deep learning » Regularization » Self supervised » Super resolution
