Image Enhancement And Target-Aware Fusion Of Infrared And Visible Images
DOI:
https://doi.org/10.63665/pbc8qe44Keywords:
Single Image Super-Resolution (SISR), Infrared Image Processing, Deep Learning, Image Reconstruction, Feature Extraction, Edge Detection, Denoising, High-Resolution Imaging, Infrared Image Enhancement, Whale Optimization Algorithm (WOA), Edge-Point Classification, Computer Vision, Image Restoration, Spatial Resolution Enhancement, Benchmark DatasetsAbstract
Single Infrared Image Super-Resolution (SISR) aims to enhance the spatial resolution of low-quality infrared images. This task is particularly challenging due to the inherent noise and limited information content in infrared images. To address these limitations, we propose a novel approach that leverages advanced deep learning techniques to effectively restore high-resolution details. Our method effectively captures and exploits the underlying structure of infrared images. By employing advanced feature extraction and reconstruction techniques, we are able to generate significantly improved image quality. Extensive experiments on various benchmark datasets demonstrate the superior performance of our proposed method in terms of both quantitative and qualitative metrics. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.
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