Panoramic Image Stitching with Efficient Brightness Fusion Using RANSAC Algorithm

Abstract

Background/Objectives: Image stitching can enhance the picture very pleasant by modifying and mixing the different aspects.Therefore, we present panoramic image stitching with efficient brightness fusion which is challenging in different bright sequences taken from different angles.Methods/Statistical analysis:For the problem of brightness, the input image is mixed with sequential images in different brightness.In this works, we proposed atechnique that blends multiple brightness using simple quality measures like color, saturation and contrast.The resulting image quality is good, and most important thing is, the method is efficient since it is simple. Thanthe resulting fused images is applied for panorama image stitching. We used multiband blending to prevent the blurring, and BRIEF (binary robust independent elementary features) method for feature descriptors. We solved the multi-image matching problem using Hamming Distanceusing the binary string based descriptors which is most similar features compare with the second most similar images.We proposed FLANN based matcher to get the more accurate results for using large datasets. We estimate Homography with the matching images using RANSAC algorithms.Findings:An effective structure is performed when we are able to resolve the brightness correction in expose too much or expose for too short a time and the appearance ghost. To solve the unification of brightness, we have collected the input images in different exposures, and selection of the good parts of each picture to an input image for stitching.We removed the blurring from input images, and solved multi-image matching using Hamming distance method. We found better results comparing other methods. For large dataset, we used FLANN based matcher, and estimated the Homography using RANSAC algorithm.Improvements/Applications:We have shown the performance of panoramic image stitching with efficient brightness fusion. We performed stitching with high regulationimages. Finally, we were able to create a panoramic image with efficient brightness fusion.

UAV visible light image feature matching based on improved whale algorithm

“…In the stage of random walking and foraging, the main behavior of the algorithm is random foraging. Therefore, in formula (14), the whale position is updated through the AD factor, which actually lacks diversity. In formula (21):…”

“…Due to the fact that ORB based feature image matching methods mostly directly match features such as nearest neighbor algorithm matching [13] or Hamming distance, these feature methods have a large number of error matching rates. To improve the accuracy of matching, a typical method is to use the RANSAC algorithm [14] to purify key points, while using the RANSAC algorithm for feature matching is difficult to obtain ideal matching parameters, especially in color image feature matching, the repeated refinement of parameters, the number of interior points, the size of subsets and datasets have a significant impact, making it difficult to apply the speed and accuracy of UAV visible light image matching in practical work. Some researchers have conducted research using different methods [15], but the matching result is not ideal.…”

Research on the processing algorithm of drilling image

“…There is a mismatch in the matching point pairs obtained by rough matching. In this paper, RANSAC [12] algorithm is used to eliminate the mismatch twice and calculate the transformation matrix. When implementing image matching, RANSAC is a convenient algorithm to use and implement.…”