Self-Convolution is a self-supervised and highly-efficient image operator that exploits non-local similarity. Self-Convolution can generalize many commonly-used non-local schemes, including block matching and non-local means.

This repo contains the Matlab code package of Self-Convolution which focuses on equivalent implementation of block matching, which includes 2D-patch and 3D-patch versions of Self-Convolution (dimension of the reference image patch). For each version, we provide a demo to show Self-Convolution can speed up the non-local denoising algorithm. To be specific, SAIST as an example method relying on 2D patches, and our proposed multi-modality image denoising method Self-MM as example of 3D patch.

The Self-Convolution functions can be plugged in any block matching based image restoration method, just follow the similar usage steps.

• 2D Patch

Example method: SAIST

Usage:

1. replace Block_matching.m function with our self_convolution_2d.m function (2d here refers to the two-dimensional search window)

2. run Denoising_Main.m (a gray-scale image denoising demo)

• 3D Patch

Example method: Self-MM

Usage: run demo_rgbnir_denoising.m (a RGB-NIR image denoising demo)

Runtime (in seconds) comparisons of non-local algorithms using BM and Self-Convolution, for denoising 512 * 512 single-channel images (first 7 rows) and 256 * 256 * q multi-channel images (last 3 rows), where BMtime% denotes the runtime portion of BM.

All the experiments are carried out in the Matlab (R2019b) environmentrunning on a PC with Intel(R) Core(TM) i9-10920K CPU 3.50GHz.

Paper available here. Long Journal Version preprint.

In case of use, please cite our publication:

L. Guo, Z. Zha, S. Ravishankar and B. Wen, "Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration," ICASSP 2021.

Bibtex:

@INPROCEEDINGS{9414124,
  author={Guo, Lanqing and Zha, Zhiyuan and Ravishankar, Saiprasad and Wen, Bihan},
  booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration}, 
  year={2021},
  volume={},
  number={},
  pages={1860-1864},
  doi={10.1109/ICASSP39728.2021.9414124}}

@article{guo2022exploiting,
  title={Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration},
  author={Guo, Lanqing and Zha, Zhiyuan and Ravishankar, Saiprasad and Wen, Bihan},
  journal={IEEE Transactions on Image Processing},
  year={2022},
  publisher={IEEE}
}