Seamless Integration: Advanced Deep Learning Techniques for Image Stitching

Abstract

This work holds an effective solution to image stitching by employing CNNs, GANs, and optical flow besides other image stitching methods. These techniques greatly improve feature extraction, registration, and warping, which are obstacles like parallax, exposure disparity, and dynamic content. Our methodology involves multiple intricate steps to ensure high-quality stitching: To provide efficient detection of the key point, applying CNNs for connection in feature extraction The usage of GANs for image alignment helps in contributing precise transformation The final step involves the process of multi-band blending that helps in seamless removal of the seam and exposure variations. Also, depth estimation takes care of the parallax and motion segmentation in dynamic scenes to incorporate all the used image components. The above-discussed approach was tested and assessed by using the UDIS-D dataset which is characterized by different scenes and high parallax. When testing through the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) the presented technique shows the best result compared to previous methods. Therefore, the proposed model attains an average PSNR of 33. 58 and an SSIM of 0. 939, compared with other prominent approaches to methodologies. The experiments performed here prove the efficiency of the proposed method of image stitching based on deep learning techniques and show its use in virtual platforms, medicine and autonomous robotic vehicles. Future studies will in turn involve enhancing the application of superior machine learning approaches with the view of widening the functionality of the system and improving the quality of images.