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Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Non-local Kalman Network for Video Compression Artifact Reduction.

Guo Lu, Xiaoyun Zhang, Wanli Ouyang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 1, 2019

Summary

This study introduces a deep non-local Kalman network to reduce video compression artifacts. The novel method enhances video quality by combining Kalman filtering with deep learning for superior artifact reduction.

Related Experiment Videos

Last Updated: Jan 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Signal Processing
Artificial Intelligence

Background:

Video compression algorithms reduce data size but introduce visual artifacts.
Lossy compression degrades video quality, necessitating artifact reduction techniques.

Purpose of the Study:

To propose a deep non-local Kalman network for effective video compression artifact reduction.
To improve the quality of compressed videos using a hybrid model-based and learning-based approach.

Main Methods:

Video restoration modeled as a Kalman filtering procedure using a deep Kalman model.
Incorporation of spatial and temporal non-local networks to leverage prior information.
Integration of deep neural networks for state estimation within the Kalman filter framework.

Main Results:

The proposed method effectively reduces compression artifacts in videos.
Experimental results on Vimeo-90k and HEVC datasets demonstrate significant improvements in video quality.
The recursive use of less noisy restored frames enhances restoration quality.

Conclusions:

The deep non-local Kalman network effectively combines model-based and learning-based methods for video artifact reduction.
The approach offers a promising solution for enhancing compressed video quality.
The method demonstrates superior performance in restoring high-quality frames.