Automated image fusion has a wide range of applications across a multitude of fields such as biomedical diagnostics, night vision, and target recognition. Automation in the field of image fusion is difficult because there are many types of imagery data that can be fused using different multi-resolution transforms. The different image fusion transforms provide coefficients for image fusion, creating a large number of possibilities. This paper seeks to understand how automation could be conceived for selected the multiresolution transform for different applications, starting in the multifocus and multi-modal image sub-domains. The study analyzes the greatest effectiveness for each sub-domain, as well as identifying one or two transforms that are most effective for image fusion. The transform techniques are compared comprehensively to find a correlation between the fusion input characteristics and the optimal transform. The assessment is completed through the use of no-reference image fusion metrics including those of information theory based, image feature based, and structural similarity based methods.
Image quality is affected by two predominant factors, noise and blur. Blur typically manifests itself as a smoothing of edges, and can be described as the convolution of an image with an unknown blur kernel. The inverse of convolution is deconvolution, a difficult process even in the absence of noise, which aims to recover the true image. Removing blur from an image has two stages: identifying or approximating the blur kernel, then performing a deconvolution of the estimated kernel and blurred image. Blur removal is often an iterative process, with successive approximations of the kernel leading to optimal results. However, it is unlikely that a given image is blurred uniformly. In real world situations most images are already blurred due to object motion or camera motion/defocus. Deconvolution, a computationally expensive process, will sharpen blurred regions, but can also degrade the regions previously unaffected by blur. To remedy the limitations of blur deconvolution, we propose a novel, modified deconvolution, using wavelet image fusion (moDuWIF), to remove blur from a no-reference image. First, we estimate the blur kernel, and then we perform a deconvolution. Finally, wavelet techniques are implemented to fuse the blurred and deblurred images. The details in the blurred image that are lost by deconvolution are recovered, and the sharpened features in the deblurred image are retained. The proposed technique is evaluated using several metrics and compared to standard approaches. Our results show that this approach has potential applications to many fields, including: medical imaging, topography, and computer vision.
Colouterine fistula is a rare complication of diverticular disease. Few cases have been reported in the literature. A wide spectrum of presentations may accompany this entity. In our experience it simulated uterine perforation at time of surgery. Diagnosis was confirmed by histopathology. Surgery has been suggested to be the treatment of choice in this situation, with resection of the colon as the main step; hysterectomy is mandatory to extirpate the sinus of infection. We undertook a conservative approach in this patient and did a simple repair of the colon. Hysterectomy was carried out. We believe that this would be a safe alternative to resection of the colon.
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