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Low-light image super-resolution reconstruction algorithm based on enhanced feature maps

delete2026-03-01
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PRE
AI
W
Wang, Le *
Z
Zhu, Yinglin
X
Xiaona Ding
DOI：10.1080/13682199.2026.2625613delete
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Abstract

Abstract

En 中文
To improve the resolution and clarity of low-light images, this paper presents a super-resolution reconstruction algorithm based on enhanced feature maps. The algorithm employs a generalized total variation model for image denoising and an improved Butterworth high-pass filter to extract high-frequency components. Weighted guided filtering is used to enhance illumination and preserve edge details, while nonlinear stretching improves saturation and contrast. A color recovery and edge-preservation mechanism based on the YCbCr color space and ER-ANR method is introduced to refine reflection components. Experimental results show that the proposed method effectively accomplishes super-resolution reconstruction with abundant details, clear visualization, and superior overall image quality.
Keywords:
Image cleaning
illumination feature estimation
colour feature enhancement
low-light images
super-resolution reconstruction

Journal

I
IMAGING SCIENCE JOURNAL
IF:
1.1
Papers:
59
Citations:
0

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