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

This is the authors' abstract. We don't add key points for this paper.

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.