## 1 Introduction  
Three-dimensional integral imaging is one of the auto-stereoscopic three-dimensional imaging techniques proposed by [PERSON] in 1908 [1]. Integral imaging offers several advantages in that it captures three-dimensional objects using white light and provides full parallax and continuous viewing points. These merits enable integral imaging to be employed in various applications using real 3D images, such as medical imaging, 3D object detection, and automation systems. Thus, many studies on integral imaging have been actively conducted [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
An integral imaging system consists of two processes: pickup and reconstruction. During the pickup process, different perspectives of a 3D object are captured as an elemental image array (EIA) using a camera array, a moving camera, or a lens array. This captured EIA is reconstructed into a 3D image using optical or computational methods. Optical reconstruction is complex and subject to various optical restrictions. To overcome these drawbacks, computational integral imaging reconstruction (CIIR) methods have been proposed [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38].  
A CIIR technique reconstructs a 3D image of an object without the limitations of 3D optical display. Since each elemental image contains angular information about a 3D object, a CIIR method can provide potential solutions to address occlusion problems. However, scattering or hazing artifacts from partial occlusion significantly impact the quality of the reconstructed images; thus, many studies have been performed to enhance the image quality [30; 31; 32; 33; 34; 35; 36; 37; 38]. These studies typically employ synthetic aperture integral imaging (SAII) to acquire an elemental image array (EIA), subsequently aiming to eliminate scattering artifacts from each elemental image before the computational reconstruction. However, an SAII method often requires a complicated pickup process involving a camera array or a moving camera, making its application system expensive in terms of both cost and space.  
In this paper, we propose a new method to reduce scattering artifacts in reconstructed images after computational reconstruction. Unlike a SAII method, where elemental images are acquired using an array of high-resolution cameras, our approach utilizes elemental images obtained through a lens array. These elemental images often contain only partial information about a 3D object and have low resolution, making them unsuitable for preprocessing. Consequently, in contrast to existing approaches, we remove scattering artifacts from the reconstructed images. The proposed method for removing scattering artifacts is based on the dehazing model via spectral analysis [38]. We also propose a new, efficient method for automatically estimating the visibility coefficient according to the signal analyses, since the visibility coefficient is the most important parameter affecting the image quality. To evaluate the proposed method, we carry out computer simulation experiments. The experimental results indicate that the proposed method improves the image quality compared with that of the CIIR images of existing methods.