# Side Window Filtering

arXiv: https://arxiv.org/abs/1905.07177 · HTML: https://mlsanity.com/paper/1905.07177/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 27 of 27)

- [Guided Depth Map Super-resolution: A Survey](https://mlsanity.com/paper/2302.09598/) (2023)
- [Detecting Generated Images by Real Images Only](https://mlsanity.com/paper/2311.00962/) (2023)
- [Unsharp Mask Guided Filtering](https://mlsanity.com/paper/2106.01428/) (2021)
- [Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects](https://mlsanity.com/paper/2107.01619/) (2021)
- [Quarter Laplacian Filter for Edge Aware Image Processing](https://mlsanity.com/paper/2101.07933/) (2021)
- [EGGS: Edge Guided Gaussian Splatting for Radiance Fields](https://mlsanity.com/paper/2404.09105/) (2024)
- [Dynamic Large Language Models on Blockchains](https://mlsanity.com/paper/2307.10549/) (2023)
- [Multilevel Large Language Models for Everyone](https://mlsanity.com/paper/2307.13221/) (2023)
- [Gradient Domain Diffusion Models for Image Synthesis](https://mlsanity.com/paper/2309.01875/) (2023)
- [Multi-Curve Translator for High-Resolution Photorealistic Image Translation](https://mlsanity.com/paper/2203.07756/) (2022)
- [Learnable Heterogeneous Convolution: Learning both topology and strength](https://mlsanity.com/paper/2301.05440/) (2023)
- [Guided Linear Upsampling](https://mlsanity.com/paper/2307.09582/) (2023)
- [Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2403.14244/) (2024)
- [TSSR: A Truncated and Signed Square Root Activation Function for Neural Networks](https://mlsanity.com/paper/2308.04832/) (2023)
- [Phase Guided Light Field for Spatial-Depth High Resolution 3D Imaging](https://mlsanity.com/paper/2311.10568/) (2023)
- [Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space](https://mlsanity.com/paper/2401.07753/) (2024)
- [A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing](https://mlsanity.com/paper/2107.07058/) (2021)
- [One-Sided Box Filter for Edge Preserving Image Smoothing](https://mlsanity.com/paper/2108.05021/) (2021)
- [Image Edge Restoring Filter](https://mlsanity.com/paper/2112.13540/) (2021)
- [When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters](https://mlsanity.com/paper/2203.00258/) (2022)
- [iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer](https://mlsanity.com/paper/2207.06831/) (2022)
- [Improved Real-time Image Smoothing with Weak Structures Preserved and High-contrast Details Removed](https://mlsanity.com/paper/2307.06298/) (2023)
- [STL: A Signed and Truncated Logarithm Activation Function for Neural Networks](https://mlsanity.com/paper/2307.16389/) (2023)
- [PLMM: Personal Large Language Models on Mobile Devices](https://mlsanity.com/paper/2309.14726/) (2023)
- [GDGS: Gradient Domain Gaussian Splatting for Sparse Representation of Radiance Fields](https://mlsanity.com/paper/2405.05446/) (2024)
- [Enabling Region-Specific Control via Lassos in Point-Based Colorization](https://mlsanity.com/paper/2412.13469/) (2024)
- [LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss](https://mlsanity.com/paper/2606.07932/) (2026)

## Abstract (arXiv)

Local windows are routinely used in computer vision and almost without exception the center of the window is aligned with the pixels being processed. We show that this conventional wisdom is not universally applicable. When a pixel is on an edge, placing the center of the window on the pixel is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we propose a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed. The SWF technique is surprisingly simple yet theoretically rooted and very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under the SWF framework. Extensive analysis and experiments show that implementing the SWF principle can significantly improve their edge preserving capabilities and achieve state of the art performances in applications such as image smoothing, denoising, enhancement, structure-preserving texture-removing, mutual-structure extraction, and HDR tone mapping. In addition to image filtering, we further show that the SWF principle can be extended to other applications involving the use of a local window. Using colorization by optimization as an example, we demonstrate that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation. Given the ubiquity of window based operations in computer vision, the new SWF technique is likely to benefit many more applications.