IMAGE PROCESSING

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In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. 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 signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement.^[1] The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. Images then could be processed in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and computer-intensive operations. With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing and generally, is used because it is not only the most versatile method, but also the cheapest.

Today will see one example application of Watermarking Security Theory and Practice

  

Watermarking Security Theory and Practice

ABSTRACT

 watermarking security based on a cryptanalysis point of view. The main idea is that information about the secret key leaks from the observations, for instance watermarked pieces of content, available to the opponent. Tools from information theory (Shannon’s mutual information and Fisher’s information matrix) can measure this leakage of information. The security level is then defined as the number of observations the attacker needs to successfully estimate the secret key. This theory is applied to two common watermarking methods: the substitutive scheme and the spread spectrum based techniques. Their security levels are calculated against three kinds of attack. The experimental work illustrates how Blind Source Separation (especially Independent Component Analysis) algorithms help the opponent exploiting this information leakage to disclose the secret carriers in the spread spectrum case. Simulations assess the security levels derived in the theoretical part of the article.

INTRODUCTION

Digital watermarking studies have always been driven by the improvement of robustness. Most of articles of this field deal with this criterion, presenting more and more impressive experimental assessments. Some key events in this quest are the use of spread spectrum the invention of resynchronization schemes the discovery of side information channel and the formulation of the opponent actions as a game On the contrary, security received little attention in the watermarking community. The first difficultyis that security and robustness are neighboring concepts, which are hardly perceived as different. The intentionality behind the attack is not enough to make a clear cut between these two concepts. An image compression is clearly an attack related to robustness, but it might happen intentionally, i.e. with the purpose of removing the watermark, or not. Robust watermarking is defined in as a communication channel multiplexed into original content in a non-perceptible way, and whose “capacity degrades as a smooth function of the degradation of the marked content”. We add that the degradation is due to a classical content processing (compression, low-pass filtering, noise addition, geometric attack The attacker has three known strategies to defeat watermark robustness: to remove enough watermark signal energy, to jam the hidden communication channel, or to desynchronize the watermarked content. T. Kalker then defines watermarking security as “the inability by unauthorized users to access [i.e. to remove, to read, or to write the hidden message] the communication channel” established by a robust watermarking. Security deals with intentional attacks whose aims are not only the removal of the watermark signal, excluding those already encompassed in the robustness category since the watermarking

technique is assumed to be robust.

  

Applications of Digital Image Processing

Image sharpening and restoration

Image sharpening and restoration refers here to process images that have been captured from the modern camera to make them a better image or to manipulate those images in way to achieve desired result. It refers to do what Photoshop usually does.This includes Zooming, blurring , sharpening , gray scale to color conversion, detecting edges and vice versa , Image retrieval and Image recognition. The common examples are:

Fig, Orignal image

Fig. Zoomed image

Fig.Blur image

Fig.Sharp image

Fig.Edges

REFERENCE ARTICLES 

1. Obstacle Detection and Collision Avoidance fora UAV With Complementary Low-Cost Sensors. 
2. TimeOptimal Maneuver Planning in Automatic Parallel Parking Using a SimultaneousDynamic Optimization Approach.
3.  Privacy-Assured Outsourcing of ImageReconstruction Service in Cloud.
4. Query-Adaptive Image Search with Hash Codes.