## 1 Introduction  
Integral imaging is a popular method to achieve a 3D display without the need to wear glasses. This approach provides full parallax and all-depth cues, enabling a more immersive experience [1; 2; 3; 4]. Previously, the pickup and display all relied on a lenslet array [5]. With the advancement of computers, computer-generated and computational reconstruction have experienced rapid progress, and many efforts have been devoted to realistic high-quality 3D images, including increasing the sampling rate to improve the resolution of each view [6], changing rendering algorithms to accelerate and generate high-quality 3D images [7; 8], as well as the use of convolutional neural networks (CNNs) to eliminate the noise of 3D images [9]. Computational integral imaging reconstruction (CIIR) is based on the technique of optical mapping [10; 11; 12], which involves mapping elemental images to a 3D space and combining them at a certain depth plane to generate a 3D scene. Optical mapping-based CIIR methods have been extensively studied for improving 3D imagery due to their straightforward ray optics model [13; 14]. These methods consist of mapping the pixels in elemental images to the 3D space, either directly or after applying windowing or convolution. Pixel mapping methods, in which each pixel is individually mapped using a lens array, effectively reduce computational costs and improve the visual quality of reconstructed images [15; 16; 17; 18; 19]. Windowing methods involve weighting the elemental images with a window function before mapping [20], which helps eliminate blurring and lens array artifacts, thereby improving the quality of the reconstructed image [21; 22]. Convolutional methods have been recently introduced to extract depth information through the use of convolutions and delta functions. This approach has resulted in improved image quality and greater control over depth resolution [23; 24; 25; 26; 27; 28; 29; 30]. The reconstruction of super high-resolution 3D images with lower optical sampling presents a substantial challenge, notwithstanding the valuable insights offered by the aforementioned research in the field of 3D image generation and reconstruction. In recent years, deep learning has become a popular technique in computer vision and has made significant progress in various areas such as speech and image recognition and classification [31], novel view synthesis [32], and image super-resolution (SR) [33]. Image SR is a technique used to restore high-resolution images with more details from low-resolution images. It aims to restore lost details, improve image clarity, and enhance visual quality, making the images more informative and realistic. In surveillance systems, it can be used to improve video quality and aid in target recognition and tracking. In medical imaging, it can assist doctors in more accurate diagnosis of patients [34; 35]. In satellite imaging and other related fields, it can enhance image clarity and analytical capabilities, helping scientists gain a better understanding of earth, weather, and astronomical phenomena. Single image super-resolution (SISR) has been the subject of extensive research over the past few decades and serves as the foundation for multiple image super-resolution [36]. Super-resolution convolutional neural networks (SRCNNs) utilize an end-to-end learning approach to directly generate high-resolution images from low-resolution ones, eliminating the need for multiple stages in traditional methods [37]. Very deep super-resolution (VDSR) [38] and residual dense networks (RDNs) [39] further explore the mechanisms and effectiveness of deep convolutional neural networks. VDSR, consisting of 20 convolutional layers, can better capture the minute details in images. On the other hand, RDNs utilize the structure of residual skip connections and dense connections to effectively handle complex textures and structures, resulting in the generation of realistic images.