A new family of Gaussian filters with adaptive lobe location and smoothing strength for efficient image restoration

Hassene Seddik

doi:10.1186/1687-6180-2014-25

Abstract

Noise can occur during image capture, transmission, or processing phases. Image de-noising is a very important step in image processing, and many approaches are developed in order to achieve this goal such as the Gaussian filter which is efficient in noise removal. Its smoothing efficiency depends on the value of its standard deviation. The mask representing the filter presents generally static weights with invariant lobe. In this paper, an adaptive de-noising approach is proposed. The proposed approach uses a Gaussian kernel with variable width and direction called adaptive Gaussian kernel (AGK). In each processed window of the image, the smoothing strength changes according to the image content, noise kind, and intensity. In addition, the location of its lobe changes in eight different directions over the processed window. This directional variability avoids averaging details by the highest mask weights in order to preserve the edges and the borders. The recovered data is de-noised efficiently without introducing blur or losing details. A comparative study with the static Gaussian filter and other recent techniques is presented to prove the efficiency of the proposed approach.

Highlights

The image de-noising remains an important goal in image or video pre-processing as a preliminary task for data transmitting, pattern recognition, etc
We propose to vary the standard deviation using the neural network which responds to the non-linear variation of the smoothing strength
The proposed method is compared to the static Gaussian filtering using the same standard deviation σopt and a support size equal to (6 × σopt + 1) × (6 × σopt + 1) (Table 1)

Summary

Introduction

The image de-noising remains an important goal in image or video pre-processing as a preliminary task for data transmitting, pattern recognition, etc. In the case of high distortions, efficient noise-removing techniques may introduce artifacts or blur the image. Image denoising techniques using the Gaussian filter has been widely used in many fields for its ability to efficiently restore degraded data. In [1], the authors combined the following three techniques: wavelet transform, curvelet transform, and the Gaussian filter to recover the distorted image. The authors in [2] exploited the relationship between linear diffusion and Gaussian scale space to estimate optimal variances and window size of the Gaussian. An efficient technique based on the Gaussian filter with dynamic structure that targets noise is

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Conclusion