Çoklu pozlamalı görüntü füzyonu kenar koruyucu yumuşatma filtresi

This master's thesis focuses on multi-exposure image fusion using image processing and deep learning techniques. The development of image edge smoothening system using CNN based on artificial bee colony optimization task is in hot pursuit, with special attention being given to the smoothening of all the edges of image. Given its high propensity to meta-size, going hand in hand with severe decreases in preservation rates, and the high inter-edge variability in image appearance, as well as a strong requirement on the training of the physician properly de-noising an image can be considered a daunting task. The purpose of this research thesis is to use a deep learning and image processing pipeline for multi-exposure image fusion for the segmentation of edges in an image using with hybrid techniques in deep learning and imaging. The literature review of different papers was conducted with different imaging model architectures. The CNN custom model was created for the task, and deep learning technique (CNN) was used with different levels of fine tuning of hybrid image processing techniques. Screening for high edge filter to identify edges at high accuracy has been under debate. In current discussion, the suggested smoothening procedure is bone density based including possible prescreening techniques using the Artificial Bee Colony (ABC) optimization. Development of new prediction models and automated smoothening could contribute to general screening programs in the future.The custom deep learning model architectures were designed to represent different depths. The idea behind this is to analyze the effect of increasing representational capacity to the results and visualizations for all edges in an image. Additionally, deep learning CNN model was created to represent traditional automated image processing approach. Image processing has been used in some edging of images, producing good results for example in segmentation of image area structure and segmentation of image smoothening areas under consideration. The study also attempts to find solutions to practical deep learning challenges such as low training speed and lack of transparency with an accuracy of 97.59% absolutely. The imaging and deep learning pipelines are optimized in order to exploit the available parallelism using the MATLAB programming language with multiple tools under consideration.