Zero-sample mural superresolution reconstruction for enhanced perceptual quality

Abstract

Abstract Aiming at the problem of texture loss and poor perceptual quality in low-resolution mural images, this paper proposes a zero-sample mural superresolution reconstruction method called EPZSSR to enhance perceptual quality, and the specific model is obtained by training the image. The algorithm takes the zero-shot superresolution method as the framework, randomly cuts the original image into a 128 * 128 size, performs Gaussian blurring on the image, uses Lanczos interpolation to downsample the smooth image to reduce artifacts, and uses convolutional attention module and skip connection to optimize the network structure. SmoothL1Loss is used to enhance the robustness of the model, and the PI value is introduced as the perceptual quality evaluation index. The experimental results show that compared with other superresolution reconstruction algorithms, the peak signal-to-noise ratio of the algorithm in this paper is increased by 0.98–3.23 dB on average. The mural texture reconstruction effect is better, the PI value is reduced by 0.56 on average, the mural perception quality is better, and the running time is reduced by 89.68 s on average. It has a certain value for mural superresolution reconstruction.

Keywords

• Zero-sample superresolution reconstruction
• Perceived quality
• Convolution attention
• Skip connection