Generalized image degradation model for removing motion blur in image sequence

1998 ◽  
Author(s):  
Yoo C. Choung ◽  
Jeong-Ho Shin ◽  
Joon-Ki Paik
Keyword(s):  
Image Sequence ◽  
Motion Blur ◽  
Image Degradation ◽  
Degradation Model ◽  
Image Degradation Model

scholarly journals An Image Degradation Model for Depth-augmented Image Editing

2015 ◽  
Vol 34 (5) ◽  
pp. 191-199 ◽  
Author(s):  
James W. Hennessey ◽  
Niloy J. Mitra
Keyword(s):  
Image Editing ◽  
Image Degradation ◽  
Degradation Model ◽  
Image Degradation Model

Behaviour of image degradation model in multiresolution

2000 ◽  
Vol 80 (11) ◽  
pp. 2407-2420 ◽  
Author(s):  
Sunil K. Kopparapu ◽  
U.B. Desai ◽  
P. Corke
Keyword(s):  
Image Degradation ◽  
Degradation Model ◽  
Image Degradation Model

scholarly journals A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Zhenfei Gu ◽  
Can Chen ◽  
Dengyin Zhang
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Image Degradation ◽  
Degradation Model ◽  
Atmospheric Scattering ◽  
Light Image ◽  
Image Prior ◽  
Proposed Model ◽  
Enhancement Method ◽  
Image Degradation Model

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.

Fast Restoration Algorithm for Rotational Motion Blurred Images

2011 ◽  
Vol 267 ◽  
pp. 969-973
Author(s):  
Jian Yang Zhou ◽  
Yin Tao Yang ◽  
Yi Liang Wu
Keyword(s):  
Image Restoration ◽  
Rotational Motion ◽  
Fast Algorithm ◽  
Degradation Model ◽  
Experimental Platform ◽  
Restoration Algorithm ◽  
Blurred Image ◽  
Comparison And Analysis ◽  
Blurred Images ◽  
Image Degradation Model

This paper first introduces the history and research status of motion-blurred image restoration, and then establishes a rotating image degradation model. we present a fast algorithm for real-time hardware restoration of rotating motion blurred images, the algorithm is simulated and experimental platform is built for the actual verification. Through comparison and analysis of the recovery results, we verify the accuracy of the algorithm, and it shows that this rotational motion blurred image restoration algorithm can receive satisfactory results.

scholarly journals Image Restoration Model Using Total Variance, Bilateral and Wavelet Denoising Filter

2019 ◽  
Vol 8 (3) ◽  
pp. 5888-5891
Keyword(s):  
Image Restoration ◽  
Bilateral Filter ◽  
Image Transmission ◽  
Motion Blur ◽  
Total Variance ◽  
Atmospheric Conditions ◽  
Image Degradation ◽  
Variance Filter ◽  
Degraded Image ◽  
Wavelet Image

Noise in images are most common due to various degradation. Noises in images are random variations in images due to lighting conditions, camera electronics, surface reflectance, lens, atmospheric conditions and motions (Either camera is moving or object is moving). Image Restoration is a process which restores a degraded image into its original image which has been degraded by some degradation model which degraded the image. Images are degraded due to various reasons. The first and foremost reason for image degradation is the fault in the imaging devices during the image acquisition process. The noise is generated in the imaging devices and is propagated to the image. The second source of degradation in image is the noise added during the image transmission. This type of image degradation is most common. The third source of image degradation is due to the motion blur and atmospheric turbulence. This paper analyzes various image noise models and restoration techniques. Particularly in analyses three kind of filters namely total variance filter, bilateral filter and wavelet image denoising. The image restoration is measured using the PSNR and SSI of original and degraded images

The Dedusting Method Based on a Single Still Image

2013 ◽  
Vol 333-335 ◽  
pp. 929-933
Author(s):  
Yuan Yu Wang ◽  
Chong Yang Yang ◽  
Xing Kui Wang
Keyword(s):  
Multiple Scattering ◽  
Depth Map ◽  
Single Image ◽  
Degradation Model ◽  
Distance Information ◽  
Still Image ◽  
First Order ◽  
Dark Channel ◽  
The Relationship ◽  
Image Degradation Model

In order to restore the image in dust environment, a method was proposed to restore the images in dust environment based on a single still image. Firstly, an image degradation model considering multiple scattering factors caused by dust was built using the first-order multiple scattering methods. Then, a new geometric depth model of single image was presented. This model utilized depth map of a dust image to improve usual geometric depth model. According to the new model the every distance of the objects in dust environment was able to acquire from a single still image. Finally, an image in dust environment could be restored according to the relationship between the dark channel prior model and the distance information which was calculated by the geometric depth model. The experimental results have shown that the method enhanced luminance and contrast. The method provided a foundation for target recognition in the dust environments.

A Motion Blurred Image Restoration Method Based on Wiener Filtering

2011 ◽  
Vol 403-408 ◽  
pp. 1664-1667 ◽  
Author(s):  
Qian Qian Quan
Keyword(s):  
Image Restoration ◽  
Motion Blur ◽  
Experimental Results ◽  
Wiener Filtering ◽  
Degradation Model ◽  
Image Blurring ◽  
Blurred Image ◽  
Working Principle ◽  
Restoration Method ◽  
Blurred Images

To the deficiencies of traditional methods for avoiding motion image blurring, a motion blur image restoration method is studied based on Wiener filtering in this paper. The formation factors of motion-blurred images and the imaging process are analyzed, and the motion blur degradation model is established. It introduced the working principle of Wiener filtering, described the steps of blurred image restoration in details. The experiment testing and data analyzing are also conducted. Experimental results showed that the method can has good performance.

scholarly journals A Study on Digital Image Restoration Filters

2020 ◽  
pp. 28-33
Author(s):  
Shaila Banu SK ◽  
Sivaparvathi B ◽  
Munwar Ali SK ◽  
Raheema SK ◽  
Sailaja R ◽  
...  
Keyword(s):  
Color Image ◽  
Random Noise ◽  
Wiener Filtering ◽  
Grayscale Image ◽  
Inverse Filtering ◽  
Degradation Model ◽  
Blurred Image ◽  
Blurring Effect ◽  
Matlab Programming ◽  
Image Degradation Model

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.

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