Digital image processing

Summary

Digital image processing is the use of a digital computer to process s through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over . It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. The generation and development of digital image processing are mainly affected by three factors: first, the development of computers; second, the development of mathematics (especially the creation and improvement of discrete mathematics theory); third, the demand for a wide range of applications in environment, agriculture, military, industry and medical science has increased. History Many of the techniques of processing, or digital picture processing as it often was called, we

Official source

https://en.wikipedia.org/wiki/Digital_image_processing

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Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Hatef Otroshi Shahreza, Seyedeh Sahar Sadrizadeh

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as prior knowledge. Moreover, we define a multi-term loss function to train our network. In particular, we define a novel term to impose the sparsity nature of impulsive noise. Experimental results indicate that our deep learning approach significantly outperforms other state-of-the-art methods in terms of reconstruction quality and speed on a system equipped with GPU. Meanwhile, we introduce a fast iterative method, as a post-processing stage, to further improve the reconstruction quality of our neural network. The proposed post-processing algorithm improves the reconstruction quality in only a fraction of a second. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

ELSEVIER2022

A parallel VLSI architecture optimized for block-based image processing

EPFL1996

This article documents the practical efforts of a group of scientists designing an imageprocessing algorithm for saliency detection. By following the actors of this computer science project, the article shows that the problems often considered to be the starting points of computational models are in fact provisional results of time-consuming, collective and highly material processes that engage habits, desires, skills and values. In the project being studied, problematization processes lead to the constitution of referential databases called ‘ground truths’ that enable both the effective shaping of algorithms and the evaluation of their performances. Working as important common touchstones for research communities in image processing, the ground truths are inherited from prior problematization processes and may be imparted to subsequent ones. The ethnographic results of this study suggest two complementary analytical perspectives on algorithms: (1) an ‘axiomatic’ perspective that understands algorithms as sets of instructions designed to solve given problems computationally in the best possible way, and (2) a ‘problem-oriented’ perspective that understands algorithms as sets of instructions designed to computationally retrieve outputs designed and designated during specific problematization processes. If the axiomatic perspective on algorithms puts the emphasis on the numerical transformations of inputs into outputs, the problem-oriented perspective puts the emphasis on the definition of both inputs and outputs

SAGE Publications2017