International Journal of Image Processing and Pattern Recognition
A Study on CNN-Based Image Mosaicing
1 Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
2 Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
Abstract
The intricate tapestry of image mosaicing, while a cornerstone of computer vision, has long been constrained by the rigidity of classical, geometry-driven pipelines. Hand-crafted feature detectors and homography estimators, while effective in controlled environments, prove brittle when confronted with the challenges of real-world scenes: significant parallax, dynamic moving objects, repetitive textures, and illumination variance. This work introduces a fundamental paradigm shift, proposing a deep convolutional neural network (CNN) architecture designed for end-to-end, perceptually-driven image mosaic generation. Our model eschews the multi-stage, error-prone traditional workflow, instead learning a latent, correspondence-aware representation directly from image data. This network is trained not just to align pixels, but to synthesize a visually coherent scene, implicitly handling occlusions, warping artifacts, and exposure inconsistencies. Through extensive evaluation on benchmark and challenging real-world datasets, our CNN-based approach demonstrates a marked improvement in quantitative metrics and qualitative visual fidelity, particularly in high-parallax and dynamic scenarios. It successfully produces seamless, ghost-free mosaics where classical methods fail. This research establishes a new state-of-the-art, proving that a holistic, data-driven learning approach can master the complex art of scene assembly, paving the way for more robust and intelligent visual stitching applications in robotics, aerial imaging, and virtual reality.
Keywords
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