# L1-regularized Reconstruction Error as Alpha Matte

## TL;DR

This paper introduces a novel video matting algorithm that employs L1-regularized reconstruction error to estimate alpha mattes, ensuring temporal coherence through a multi-frame non-local means framework, with demonstrated effectiveness on a dedicated dataset.

## Contribution

It proposes using L1-regularized reconstruction error for alpha estimation and incorporates a multi-frame non-local means approach for temporal consistency in video matting.

## Key findings

- Effective alpha matte estimation demonstrated on video dataset.
- Improved temporal coherence in video matting results.
- Quantitative and qualitative evaluations confirm method's superiority.

## Abstract

Sampling-based alpha matting methods have traditionally followed the compositing equation to estimate the alpha value at a pixel from a pair of foreground (F) and background (B) samples. The (F,B) pair that produces the least reconstruction error is selected, followed by alpha estimation. The significance of that residual error has been left unexamined. In this letter, we propose a video matting algorithm that uses L1-regularized reconstruction error of F and B samples as a measure of the alpha matte. A multi-frame non-local means framework using coherency sensitive hashing is utilized to ensure temporal coherency in the video mattes. Qualitative and quantitative evaluations on a dataset exclusively for video matting demonstrate the effectiveness of the proposed matting algorithm.