# Basis Prediction Networks for Effective Burst Denoising with Large Kernels

arXiv: https://arxiv.org/abs/1912.04421 · HTML: https://mlsanity.com/paper/1912.04421/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 30 of 46)

- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [NAN: Noise-Aware NeRFs for Burst-Denoising](https://mlsanity.com/paper/2204.04668/) (2022)
- [Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication](https://mlsanity.com/paper/2212.08273/) (2022)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [KBNet: Kernel Basis Network for Image Restoration](https://mlsanity.com/paper/2303.02881/) (2023)
- [Burstormer: Burst Image Restoration and Enhancement Transformer](https://mlsanity.com/paper/2304.01194/) (2023)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [A Unified Conditional Framework for Diffusion-based Image Restoration](https://mlsanity.com/paper/2305.20049/) (2023)
- [Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2104.06191/) (2021)
- [Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images](https://mlsanity.com/paper/2101.09870/) (2021)
- [Self-Supervised Image Restoration with Blurry and Noisy Pairs](https://mlsanity.com/paper/2211.07317/) (2022)
- [Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network](https://mlsanity.com/paper/2205.04721/) (2022)
- [D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration](https://mlsanity.com/paper/2207.03294/) (2022)
- [MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results](https://mlsanity.com/paper/2209.07052/) (2022)
- [NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results](https://mlsanity.com/paper/2505.12089/) (2025)
- [Real-time Streaming Video Denoising with Bidirectional Buffers](https://mlsanity.com/paper/2207.06937/) (2022)
- [RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection](https://mlsanity.com/paper/2211.04203/) (2022)
- [Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement](https://mlsanity.com/paper/2304.06703/) (2023)
- [Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition](https://mlsanity.com/paper/2102.01026/) (2021)
- [A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift](https://mlsanity.com/paper/2203.09294/) (2022)
- [Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution](https://mlsanity.com/paper/2207.01797/) (2022)
- [Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks](https://mlsanity.com/paper/2308.02947/) (2023)
- [Recurrent Self-Supervised Video Denoising with Denser Receptive Field](https://mlsanity.com/paper/2308.03608/) (2023)
- [Spatial-Frequency Attention for Image Denoising](https://mlsanity.com/paper/2302.13598/) (2023)
- [Dual-Camera Joint Deblurring-Denoising](https://mlsanity.com/paper/2309.08826/) (2023)
- [DarkVision: A Benchmark for Low-light Image/Video Perception](https://mlsanity.com/paper/2301.06269/) (2023)
- [Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations](https://mlsanity.com/paper/2303.18139/) (2023)
- [Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration](https://mlsanity.com/paper/2309.01627/) (2023)

## Abstract (arXiv)

Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis elements. To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels -- shared within the image -- and the corresponding mixing coefficients -- which are specific to individual pixels. Compared to state-of-the-art techniques that output a large tensor of per-pixel spatiotemporal kernels, our formulation substantially reduces the dimensionality of the network output. This allows us to effectively exploit comparatively larger denoising kernels, achieving both significant quality improvements (over 1dB PSNR) and faster run-times over state-of-the-art methods.