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

Hongjun Zhu1, Wanjun Wang1,2, Chunyan Ma1,3

  • 1School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu 241199, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

VMESR enhances automotive camera image quality in bad weather using a novel super-resolution network. This variable mamba-enhanced super-resolution (VMESR) method improves detail recovery and perception for safer autonomous driving.

Keywords:
automotive sensorsautomotive vision systemsautonomous drivingfeature fusionmulti-scale feature extractorsuper-resolution

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Processing

Background:

  • Automotive front-view cameras are crucial for autonomous driving but suffer image degradation in adverse conditions like rain, fog, and low light.
  • Existing super-resolution methods struggle to balance performance, efficiency, and detail recovery for real-time automotive applications.

Purpose of the Study:

  • To develop an efficient and effective super-resolution network for automotive vision systems.
  • To improve the recovery of high-frequency details in degraded images captured by automotive cameras.

Main Methods:

  • Proposed VMESR (Variable Mamba-Enhanced Super-Resolution), a lightweight network integrating a selective state-space model.
  • Utilized variable-depth mamba blocks for efficient capture of long-range dependencies with linear complexity.
  • Incorporated a multi-scale feature extractor, enhanced residual modules with attention, and dense fusion for detailed feature recovery.

Main Results:

  • VMESR achieved competitive performance against state-of-the-art methods in objective metrics and perceptual quality.
  • Demonstrated significant reductions in parameter count and computational cost compared to existing solutions.
  • Validated the network's ability to recover high-frequency details effectively.

Conclusions:

  • VMESR offers a practical balance between efficiency and reconstructive accuracy for automotive super-resolution.
  • The proposed method is a deployable solution for embedded automotive sensors, enhancing autonomous driving perception robustness.
  • VMESR addresses the critical need for high-quality visual data in challenging driving environments.