# A Dictionary Based Approach for Removing Out-of-Focus Blur

arXiv: https://arxiv.org/abs/2406.11330 · HTML: https://mlsanity.com/paper/2406.11330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Iterative Filter Adaptive Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2108.13610/) (2021)
- [BLADE: Filter Learning for General Purpose Computational Photography](https://mlsanity.com/paper/1711.10700/) (2017)
- [Fast, Trainable, Multiscale Denoising](https://mlsanity.com/paper/1802.06130/) (2018)
- [SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network](https://mlsanity.com/paper/2206.12930/) (2022)
- [Real-time Blind Deblurring Based on Lightweight Deep-Wiener-Network](https://mlsanity.com/paper/2211.16356/) (2022)

## Abstract (arXiv)

The field of image deblurring has seen tremendous progress with the rise of deep learning models. These models, albeit efficient, are computationally expensive and energy consuming. Dictionary based learning approaches have shown promising results in image denoising and Single Image Super-Resolution. We propose an extension of the Rapid and Accurate Image Super-Resolution (RAISR) algorithm introduced by Isidoro, Romano and Milanfar for the task of out-of-focus blur removal. We define a sharpness quality measure which aligns well with the perceptual quality of an image. A metric based blending strategy based on asset allocation management is also proposed. Our method demonstrates an average increase of approximately 13% (PSNR) and 10% (SSIM) compared to popular deblurring methods. Furthermore, our blending scheme curtails ringing artefacts post restoration.