Restoration of foggy and motion-blurred road scenes

Veeramani, T.; Rajagopalan, A. N.; Seetharaman, Guna

doi:10.1109/icip.2013.6738192

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Veeramani, T., Rajagopalan, A.N. and Seetharaman, G., [5] worked on image defogging technique that can restore contrast loss and yield a rough estimate of the depth map of a scene. Imaging devices has attracted considerable attention to motion blur but this has not been addressed in the context of images captured under foggy conditions.…”

Section: Median Filtermentioning

confidence: 99%

An Extensive Estimation and Analysis of Image Denoising

Rajput¹,

Tomar²,

Gour³

2015

IJCA

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Image denoising is the technique of removal of the noise from the image contaminated by additive Gaussian noise without loss of features of image. It is a fundamental process in pattern recognition and image processing. When an Image is captured many factors such as lighting spectra, source, intensity and camera Characteristics affect the image. The main factor that reduces the quality of the image is Noise. It hides the important information of images and changes value of image pixels at key locations causing blurring and various other deformities. Noises must be removed from the images without loss of any information with it. Noise removal is the preprocessing stage of image processing. There are many types of noises which may corrupt the images. These noises are appear on images in many ways: at the time of acquisition due to noisy sensors, due to defective scanner or due to faulty digital camera device, as a result of transmission channel errors, due to corrupted storage media. There are numerous researches have been done on wavelet based denoising for estimation of parameters such as variance of the multi scale Linear minimum mean square error. In this review paper we have presented an extensive analysis and literature review on image denoising.

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Section: Median Filtermentioning

confidence: 99%

An Extensive Estimation and Analysis of Image Denoising

Rajput¹,

Tomar²,

Gour³

2015

IJCA

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“…It then implies the global minima for each layer via sparse Cholesky factorisation systems [11]. Veeramani et al [12] restored the foggy motionblurred images using the depth cues derived from the fog itself. It elaborates road scene of foggy images that are segmented into road, right, left and sky planes, and all the planes deblurred individually.…”

Section: Introductionmentioning

confidence: 99%

Image Defogging by Multiscale Depth Fusion and Hybrid Scattering Model

Kumar¹,

Chourasia²,

Kurmi³

2016

IJCA

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The season affecting the imaging of the hill station highly and all other reasons moreover time to time. The fog in image is significantly affecting weather issue. This paper compares the hybrid scattering model and multiscale fusion method. For the single scattering of light dominated pixels the single scattering physics model is used in the hybrid model and for the remaining pixels the multiple scattering physics model (MSPM) is used. The optical thickness is the basic parameter for this pixel identification. The fusion method is as an energy minimization based method that depends on spatial Markov model. The multiscale depth fusion method (ILMRF) embeds the fusion scheme into adaptive Markov regularization to achieve better estimation of depth map. The result of the multiscale fusion is better as compared to the hybrid methodology.

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