# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 1 Introduction  
Due to the limited display range of a single image taken by a single lens, an excellent method for stitching and merging two or more adjacent images is required in the fields of remote sensing and computer vision. A large number of scholars are working on the research of image stitching and have achieved certain results [1; 2; 3; 4]. Common image mosaicking algorithms comprise feature points detection [5], image registration, rectification, image stitching and blending. If the images are all identically exposed and perfectly registered, the stitching process will be a simple problem. However, due to non-ideal factors such as the accuracy limitations of the registration algorithm and differences in image exposure, there will exist discontinuous edges and unnatural seams in overlap regions [6]. Besides, target movements may also cause blur or ghosting. Therefore, further processing is essential to eliminate these discontinuities and obtain a natural transition between images. Alpha blending which is known as feathering or linear blending has good performance in processing seamless caused by luminance difference. But this approach will cause blurring of high-frequency detail if there are some small registration errors [3]. The multi-band blending algorithm has better effects by using different weights at different frequencies for fusion, but it cannot correct mismatches either. Compared with the blending-related methods mentioned above, the more effective method is to find the optimal seamless in the overlapping area to bypass obvious dislocations and discontinuous areas, improving the quality of stitching. In the past three decades, a number of researchers have sought to find the optimal seamless. The seamline detection algorithm proposed by [PERSON] may be the beginning of this field, and hedefined the seamlines as the pixels that minimize the sum of the gray level differences in the overlapping area [7]. [PERSON] proposed a seamline selection method using twin snakes based on high color similarity and high texture similarity of images [8]. [PERSON]'s algorithm is a widely used algorithm to find the shortest path [9], which is also popular in the optimal seamlines detection field. [PERSON] et al. first determined the required maximum difference level and then applied [PERSON]'s algorithm to find the best seamline [10]. [PERSON] et al. proposed a novel method for searching for an optimal seamline, which was driven by seams to obtain high-quality stitching results [11]. [PERSON] et al. first introduced the optical flow to the energy function in order to find an optimal seamline for unmanned aerial vehicle images [12]. [PERSON] et al. presented an optimal seamline detection technique via convolutional neural network and graph cuts [13]. The method is effective in the field of reducing the mismatched objects of urban remote-sensing images. We found that these methods currently have used many different criteria to find the optimal seamline and achieved good results. However, most of these methods seem to ignore one objective fact, that the subject of evaluating mosaic image quality is the human visual system (HVS) itself [14]. HVS has been proven to evaluate the quality of mosaic images [15]. As far as we know, only [PERSON] et al. used visual saliency to describe visual attention in optimal seamline selection [1]. [PERSON] et al. proposed a new energy function with visual non-linearity and non-uniformity to find the optimal seam-cutting which is more consistent with the human perception [16]. However, the above methods only considered a few characteristics in HVS. HVS is a complex optical imaging and processing system that includes not only visual attention but also visual discrimination. In this paper, an image-stitching method based on visual attention and discrimination is proposed to seek out an optimal seamline that brings less discontinuous edges, many of which are masked or located in where the human eyes pay little attention or can hardly identify.