# VMESR: Variable Mamba-Enhanced Super-Resolution for Real-Time Road Scene Understanding with Automotive Vision Sensors

## TL;DR

VMESR is a new super-resolution method that improves image quality for automotive cameras in challenging conditions, balancing efficiency and accuracy for real-time use.

## Contribution

VMESR introduces a variable mamba-enhanced architecture for efficient and accurate image super-resolution in automotive vision.

## Key findings

- VMESR achieves competitive performance in objective metrics and perceptual quality compared to state-of-the-art methods.
- The method significantly reduces parameter counts and computational cost while maintaining high reconstructive accuracy.
- VMESR enhances the robustness of autonomous driving perception pipelines for embedded automotive sensors.

## Abstract

Automotive vision systems depend critically on front-view cameras, whose image quality frequently degrades under adverse conditions such as rain, fog, low illumination, and rapid motion. To address this challenge, we propose VMESR, a variable mamba-enhanced super-resolution network that integrates a selective state-space model into a lightweight super-resolution architecture. By serializing 2D feature maps and applying variable-depth mamba blocks, VMESR captures long-range dependencies with linear complexity. A multi-scale feature extractor, enhanced residual modules equipped with a convolutional block attention module, and dense fusion connections work together to improve the recovery of high-frequency details. Extensive experiments demonstrate that VMESR achieves competitive performance in both objective metrics and perceptual quality compared to state-of-the-art methods, while significantly reducing parameter counts and computational cost. VMESR provides a practical balance between efficiency and reconstructive accuracy, offering a deployable super-resolution solution for embedded automotive sensors and enhancing the robustness of autonomous driving perception pipelines.