Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework

Medical imaging is the promising area in digital image processing. Medical images are useful for all types of medical treatment and diagnostics. Medical images are captured through the medical devices, consists some kind of noises and it requires efficient enhancement techniques. Medical imaging also useful in the image segmentation and object detection purposes. Various researcher proposed several types of enhancement techniques and edge detection techniques, but still accuracy and noise are challenge for the enhanced image. So, it is the need of some intelligent techniques to address these issues. In this work we proposed deep learning-based convolution neural network for the image denoising and image enhancement and for the edge detection fuzzy logic-based approach used. The model of DnCNN used here for the image denoising and image enhancement, this model comprises several convolution layers along with input and output layer, this model learns according to the weights and bias. Also, fuzzy logic technique implemented fuzzy inference rules which can give more accurate edges of the image. The result obtained through this hybrid approach is very interesting and effective as compare with previous approaches like histogram-based approach and linear filtering approach. Proposed methods give the promising results as compare with existing methods. All types of simulation performed in MATLAB 2020.