Fiber bundle (FB) based endoscopes are indispensable in biology and medical science due to their minimal invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs such as low numerical aperture (NA) and individual fiber core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilizing Generative Adversarial Networks (GANs). In order to train our deep learning model, we built a FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively converted wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fiber bundle-based fluorescence microscopy. Keywords: Fiber Bundle-Based Fluorescence microscopy, Deep Learning Model, GAN, Image-to-Image Translation, Multifocal Structured Illumination Microscopy (MSIM), Biological Imaging.