Image Enhancement Based on Abstraction and Neural Network
نویسندگان
چکیده
this paper presents a hybrid technique for image enhancement with ability of de-noising it integrates two different processing aspects into one. The proposed algorithm uses the image abstraction technique for detecting the information density in different parts of image then accordingly operates the smoothing filter and after filtering the information’s of edges are recombined with the filtered image. The proposed technique also utilizes the Neural Network for filtering noise generated edge patterns. Hence the approach not only enhances the image but also avoids the enhancement of noise. The simulation of algorithm shows that it improves the perception; remove noise while maintaining the structure information intact it is also found that the proposed technique is quite fast.
منابع مشابه
Image Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution
In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...
متن کاملA Neural Network Based Image Abstraction Technique
image abstraction is a method of enhancing or highlighting the visualization of main information contents of image and smoothing the other portion. It is quite important for many multimedia applications like cartooning and animation where symbolic presentation matters; hence it is an important part in the field of image processing. In this paper we are proposing a neural network based approach ...
متن کاملA Deep Model for Super-resolution Enhancement from a Single Image
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...
متن کاملA Radon-based Convolutional Neural Network for Medical Image Retrieval
Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...
متن کاملQuad-pixel edge detection using neural network
One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. Several edge detectors have been developed in the past decades, although no single edge detector...
متن کامل