Exploring Image Processing and Image Restoration Techniques

Image Restoration is one of the challenging and essential milestones in the image processing domain. Digital image processing is a technique for manipulating digital images using a variety of computer algorithms. The process of transforming the degraded or damaged image to the original image can be known as Image Restoration. The image restoration process improves image quality by converting the degraded image into the original clean image. The techniques for image restoration are comprised of predefined parameters through which digital image gets processed for refinements. The purpose of restoration is to start with the acquired image and then estimate the original image as accurately as possible. A degraded image can be contaminated by any of a blur or noise or both. Many factors can contribute to image degradation, including poor capture, poor lighting, and poor eyesight. Medical science, defensive sensor systems, forensic detections, and astrology all rely on image restoration for accuracy. This paper discusses various image restoration techniques using recent trends for performance improvements.

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Comparative study of image restoration techniques in forensic image processing

10.1117/12.267174 ◽

1997 ◽

Author(s):

Jurrien Bijhold ◽

Arjan Kuijper ◽

Jaap-Harm Westhuis

Keyword(s):

Image Processing ◽

Comparative Study ◽

Image Restoration ◽

Restoration Techniques

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Fast Transforms in Image Processing: Compression, Restoration, and Resampling

Advances in Electrical Engineering ◽

10.1155/2014/276241 ◽

2014 ◽

Vol 2014 ◽

pp. 1-23 ◽

Cited By ~ 6

Author(s):

Leonid P. Yaroslavsky

Keyword(s):

Image Processing ◽

Image Compression ◽

Image Restoration ◽

Compressive Sensing ◽

Adaptive Filters ◽

Processing Methods ◽

Fast Transforms ◽

Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37965 ◽

2021 ◽

Vol 9 (9) ◽

pp. 301-313

Author(s):

Vimal Chauhan

Keyword(s):

Image Processing ◽

Image Restoration ◽

Median Filter ◽

Processing Technique ◽

Image Processing Technique ◽

Digital Cameras ◽

Salt And Pepper Noise ◽

Satellite Imaging ◽

Mean Filter ◽

Salt And Pepper

Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

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Study of Noise Removal Techniques for Digital Images

Biometrics ◽

10.4018/978-1-5225-0983-7.ch044 ◽

2017 ◽

pp. 1105-1144

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

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Study of Noise Removal Techniques for Digital Images

Emerging Technologies in Intelligent Applications for Image and Video Processing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9685-3.ch003 ◽

2016 ◽

pp. 48-87

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

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PDE-Based Image Processing

3-D Surface Geometry and Reconstruction ◽

10.4018/978-1-4666-0113-0.ch003 ◽

2012 ◽

pp. 49-89

Author(s):

Rajeev Srivastava

Keyword(s):

Image Processing ◽

Image Restoration ◽

Shape From Shading ◽

Surface Geometry ◽

Partial Differential ◽

Basic Concepts ◽

Processing Techniques ◽

Image Modelling

This chapter describes the basic concepts of partial differential equations (PDEs) based image modelling and their applications to image restoration. The general basic concepts of partial differential equation (PDE)-based image modelling and processing techniques are discussed for image restoration problems. These techniques can also be used in the design and development of efficient tools for various image processing and vision related tasks such as restoration, enhancement, segmentation, registration, inpainting, shape from shading, 3D reconstruction of objects from multiple views, and many more. As a case study, the topic in consideration is oriented towards image restoration using PDEs formalism since image restoration is considered to be an important pre-processing task for 3D surface geometry, reconstruction, and many other applications. An image may be subjected to various types of noises during its acquisition leading to degraded quality of the image, and hence, the noise must be reduced. The noise may be additive or multiplicative in nature. Here, the PDE-based models for removal of both types of noises are discussed. As examples, some PDE-based schemes have been implemented and their comparative study with other existing techniques has also been presented.

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