Abstract
This paper discusses about the noise reduction of images using the convolution matrix. The convolution kernel matrix filters generate new features of the input images with good quality.The noise reduction methods based on convolution kernel is achieved by deep learning theory along with the difference equations. The random variation of the colour and brightness are taken as authenticated coefficients of the images. Convolution techniques along withrecurrent neuralnetwork are applied into theinput image. This input image is divided into the matrix of pixel values. The optimal enhanced image is arrived through convolution kernel using computational learning of autonomous difference equations.
Highlights
Many researchers have proposed various research concepts using filters for noise reduction in both signal and image processing [7]
The fast growth of deep learning theory helps in noise reduction techinques based on convolution kernel achieved good results that effects in many fields such as probability, statistics, computer vision, image processing, signal processing and electrical engineering [4]
The proposed a filtration of pixel value of image using convolution filter kernel to extract feature matrix
Summary
Many researchers have proposed various research concepts using filters for noise reduction in both signal and image processing [7]. Different noise reduction methods were planned and implemented for different types of noise [1, 10]. The fast growth of deep learning theory helps in noise reduction techinques based on convolution kernel achieved good results that effects in many fields such as probability, statistics, computer vision, image processing, signal processing and electrical engineering [4]. Strong shifts in the contour are too greatly influenced by traditional contour approximation methods such as polygonal approximations.
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