Research On Image Quality Improvement And Stitching Algorithm Of Optical Fiber Image Bundle

Due to its passive,electromagnetic interference resistant,high temperature resistant,and arbitrary bending characteristics,optical fiber image bundle-based imaging systems are a lowcost alternative in some special and complex environments.However,due to the inherent characteristics of optical fiber image bundle,the images acquired by optical fiber image bundle-based imaging systems suffer from honeycomb artefacts and a small imaging field of view.To address the problem of honeycomb artefacts in optical fibre imaging,an adaptive honeycomb artefact restoration algorithm consisting of a splitting module,a restoration module and a reconstruction module is designed to restore the image elements located in the artefact region according to their generation mechanism and related characteristics.Firstly,a splitting module is used to reduce the width of the area occupied by the honeycomb artefacts in the image,then the image elements belonging to the artefact region are selected according to the designed adaptive threshold mechanism and repaired according to the designed adaptive compensation mechanism,and finally the repaired sub-images are reconstructed using the reconstruction module to obtain a high-resolution image.The proposed method achieves the highest peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)of 40.67 d B and 0.96 respectively,with an improvement of 13.98 d B in PSNR and 0.37 in SSIM compared to the unrestored image with honeycomb artefacts,and the proposed method achieves the better restoration results when compared with various methods.To address the problem of the small field of view of optical fiber image bundle imaging,image stitching is used to obtain images with a larger field of view.Based on the unsupervised image stitching algorithm UDIS-RSFTI,several adaptations are made to make it more suitable for the image stitching task of fibre-optic beam imaging.Firstly,by designing a more efficient feature extraction network and introducing a positional attention mechanism,the prediction accuracy of the single-strain matrix in the coarse alignment stage is improved while significantly reducing the number of model parameters and computational effort.Secondly,the network structure in the reconstruction stage is modified using a more efficient convolution module to further improve the inference speed while maintaining the model performance.Compared with the original network,the number of model parameters and FLOPs in this thesis are lower,with the number of model parameters and FLOPs in the coarse alignment phase being 5.68% and 56.6% of the original network,respectively,and the number of model parameters and FLOPs in the reconstruction phase being 40% and 11.43% of the original network,respectively,and the model parameters and FLOPs in the public datasets Warped COCO and UDIS-D datasets and the reconstructed network consisting of images acquired by a fibre-optic transmission beam imaging system are lower.The algorithm achieved the highest PSNR and SSIM values in the Warped COCO and UDIS-D datasets and in the dataset consisting of images acquired by a fiber optic beam imaging system,with PSNR and SSIM of27.2 d B and 0.862,respectively,an improvement of 5.18% and 5.12% compared to the original network.In comparison with other image stitching algorithms,the algorithm also achieved the highest PSNR and SSIM values and the best stitching effect,effectively extending the field of view of the optical fiber image bundle imaging images.In this thesis,we use the image acquisition platform of the optical fiber image bundle to collect image data,and then test the proposed algorithm by the designed optical fiber image bundle imaging quality enhancement and stitching system.The experimental results show that the algorithm designed in this thesis can effectively improve the image quality and field of view of the optical fiber image bundle,and has good reliability and robustness in practical applications.