scholarly journals A Comparative Study to Noise Models and Image Restoration Techniques

2016 ◽  
Vol 149 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Prabhishek Singh ◽  
Raj Shree
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Restoration Techniques ◽  
Noise Models

scholarly journals Comparative Study of Major Image Enhancement Algorithms

2017 ◽  
Vol 2 (7) ◽  
pp. 23
Author(s):  
Amrutha Kulkarni ◽  
Shanta Rangaswamy ◽  
Manonmani S
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Image Enhancement ◽  
Original Image ◽  
Inverse Process ◽  
Restoration Techniques

Image restoration is a process of reconstruction or recovery of an image that has been corrupted or degraded by any degradation phenomenon. Image restoration techniques are inclined towards modeling the degradation and applying the inverse process in order to recover the original image. The critical goal of restoration techniques is to improve the quality of an image in some predefined manner. This present paper is a comparative study of image enhancement techniques used for improving the quality of a given image and evaluate it against the quality of a given image and evaluate it against SNR, PSNR, MSE, and SSIM as metrics.

scholarly journals Analysis and Comparative Study of Image Restoration by using Matlab

2018 ◽  
Vol Volume-2 (Issue-4) ◽  
pp. 1259-1263
Author(s):  
Vaishali Kumari ◽  
Ranjan Kumar Singh ◽  
Keyword(s):  
Comparative Study ◽  
Image Restoration

The Fundamentals of Medical Image Restoration

2019 ◽  
pp. 81-99
Author(s):  
Kirti Raj Bhatele ◽  
Devanshu Tiwari
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Relative Motion ◽  
Optical System ◽  
Research Work ◽  
Aerial Photographs ◽  
Degradation Model ◽  
Blurring Effect ◽  
The Given ◽  
Noise Models

This chapter simply encapsulates the basics of image restoration, various noise models, and degradation model including some blur and image restoration filters. The mining of high resolution information from the low-resolution images is a very vital task in several applications of digital image processing. In recent times, a lot of research work has been carried out in this field in order to improve the resolution of real medical images especially when the given images are corrupted with some kind of noise. The displayed images are the result of the various stages that might cause imperfections in the digital images, for instance the so-called imaging and capturing process can itself degrade the original scene. The imperfections present in the image need to be studied and analyzed if the noise present in the images is not modelled properly. There are different types of degradations which are considered such as noise, geometrical degradations, imperfections (due to improper illumination and color), and blur. Blurring in the images is generally caused by the relative motion between the camera and the original object being captured or due to poor focusing of an optical system. In the production of aerial photographs for remote sensing purposes, blurs are introduced by the atmospheric turbulence, aberrations in the optical system, and relative motion between the camera and the ground. Apart from the blurring effect, noise also creates imperfections in the images that corrupt the images under analysis. The noise may be introduced by several factors (e.g., medium, recording or capturing system, or by the quantization process). Due to this noise or blur present in the images, resolution needs to be improved and the image is to be restored from the geometrically warped, blurred, and noisy images.

A Comparative Study of Image Deblurring Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4571-4579
Author(s):  
Rajbir Singh ◽  
Sumit Bansal
Keyword(s):  
Comparative Study ◽  
Image Restoration ◽  
Digital Image ◽  
Minimum Mean Square Error ◽  
Direct Methods ◽  
Structural Similarity ◽  
Single Step ◽  
Noise Model ◽  
Image Deconvolution ◽  
Indirect Methods

The method of recovering a true image from degraded one, to analyze that digital image and characteristics with no artifact errors is known as Image Restoration. These techniques are of two types: direct methods and indirect methods. Direct methods are those in which the results of image restoration are produced in one single step. Indirect methods are those in which the results of image restoration are produced after various steps. This method is termed as blind image deconvolution, when the known info is just the blurred digital image and no info about the (Point Spread Function) (PSF) or the degrading model. The target of the procedure is to recover both the latent (un-blurred) image and the blur kernel, simultaneously. In this paper, we presented a comprehensive research of image noise model,de-blurring methods, blur types, and a comparative study of various deblurring methods. We have implemented number experiments to study these methods according to their performance, (Peak Signal to Noise Ratio) PSNR, (structural similarity) SSIM, blur type, and (Minimum Mean Square Error) MMSE.

scholarly journals Image Dehazing Based on Physical Model and Gray Projection

2019 ◽  
Vol 9 (1S3) ◽  
pp. 241-245
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel ◽  
Restoration Techniques ◽  
Image Computation ◽  
Computing Speed ◽  
Atmospheric Light

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.

scholarly journals Exploring Image Processing and Image Restoration Techniques

2015 ◽  
Vol 15 (3) ◽  
pp. 172-179
Author(s):  
Batyrkhan Sultanovich Omarov ◽  
Aigerim Bakatkaliyevna Altayeva ◽  
Young Im Cho
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Restoration Techniques
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