Image Enhancement is the first and foremost step that has to be done in all image processing applications. It is used to improve the quality of digital images. Microscopic images are prone to addition of noise from various sources such as error in camera calibration, excess staining of microscopic slides, etc.; Image de-noising is an enhancement technique that is used to remove noise present in...

Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were s...

Signal de-noising is one of the classical problems in the field of signal processing. As a new signal processing tools, wavelet analysis, which has excellent noise performance, has caused growing concern and attention. The wavelet threshold de-noising has been researched systematacially, and the wavelet de-noising method is used on the GPS signal, which has achieved very good results.

Patch-based de-noising algorithms and patch manifold smoothing have emerged as efficient de-noising methods. This paper provides a new insight on these methods, such as the Non Local Means [1] or the image graph de-noising [8], by showing its use for filtering a selected pattern. K ̄ eywords: NL-Means, diffusion processes, diffusion geometry, graph filtering, patch manifold.

Over the past decade wavelet transforms have received a lot of attention from researchers in many diierent areas. Both discrete and continuous wavelet transforms have shown great promises in such diverse elds as image compression, image De-noising, signal processing, computer graphics, and pattern recognition, to name a few. Most of the work has been done on scalar wavelets, i.e., wavelets gene...

There exist various image de-noising techniques. Amongst them orthogonal wavelet is preferred one. However, the orthogonal wavelet transform is not better technique as proper clustering of wavelet coefficients is not possible in this technique. So a better image de-noising technique is needed to have a better SNR and greater image information. In this work, image de-noising by linear minimum me...

A critical issue in the image restoration is the problem of de-noising images while keeping the integrity of relevant image information. A large number of image denoising techniques are proposed to remove noise. Mainly these techniques are depends upon the type of noise present in images. So image de-noising still remains a important challenge for researchers because de-noising techniques remov...