# Real-time Image Smoothing via Iterative Least Squares

arXiv: https://arxiv.org/abs/2003.07504 · HTML: https://mlsanity.com/paper/2003.07504/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Contrastive Semantic-Guided Image Smoothing Network](https://mlsanity.com/paper/2209.00977/) (2022)
- [Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework](https://mlsanity.com/paper/2104.01534/) (2021)
- [Metropolis Theorem and Its Applications in Single Image Detail Enhancement](https://mlsanity.com/paper/2302.09762/) (2023)
- [Pyramid Texture Filtering](https://mlsanity.com/paper/2305.06525/) (2023)
- [A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing](https://mlsanity.com/paper/2107.07058/) (2021)
- [Reverse image filtering using total derivative approximation and accelerated gradient descent](https://mlsanity.com/paper/2112.04121/) (2021)
- [Improved Real-time Image Smoothing with Weak Structures Preserved and High-contrast Details Removed](https://mlsanity.com/paper/2307.06298/) (2023)
- [Generative Texture Filtering](https://mlsanity.com/paper/2604.19039/) (2026)

## Abstract (arXiv)

Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the propose method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution (1920×1080) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at urlhttps://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares