Previous Research

Nowadays, image processing techniques have been well developed, but there are still some bottlenecks that have not been solved. Many image processing algorithms cannot work well in a noisy environment; therefore, the noise removal algorithm is adopted as a preprocessing module. A number of approaches have been developed for noise removal and listed as follows.

1.3.1. Existing Impulse Noise Removal Algorithms

The nonlinear filtering technique, standard median (SM) [1]−[2] filter, based on order statistic has been demonstrated generally superior to linear filtering (moving average) on suppressing impulse noise. However, median filter still tends to blur fine details and destroy edges while removing out the impulse noise. To achieve better performance, median filter has been modified in many ways, such as weighted median (WM) [3]−[4], center weight median filters (CWM) [5], adaptive-length median filter [6], the recursive medians [7]−[8] and the alpha-trimmed mean filter [9]. They were expected to increase the signal preservation but relatively decrease the noise suppression ability. Applying these algorithms altogether across the whole image without identification would inevitably remove the uncorrupted detail pixels, destroy the image quality, and cause additional blur.

For that reason, the decision-making schemes [10]−[12] were proposed in which only the identified noisy pixels are processed. Besides, the switching schemes [13]−

[17] provide adaptive decision to recover the noisy pixels based on several filters to remain noise-free pixels unchanged. In [18], the progressive switching median filter was proposed for only fixed-valued impulse noise removal. Also, the weighting-average linear combinations of nonlinear median-based filters through learning-rule optimization have been proposed [19]−[21]. Although satisfactory results have been obtained, they tend to remove fine details or retain too much of the noise due to undetection or misdetection of the noise [22]. In addition, since the noisy pixels are replaced without taking into account local features, details and edges are not recovered satisfactorily, especially when the noise level is high. The thresholding filtering [23] which composed of new efficient noise detectors was proposed to prevent the misclassification of noise-free pixels. The edge-directed noise detection

and suppression strategy was proposed to preserve the details and edges [24]. Two stage approaches that combine noise identification and edge preserving supplementary have been proposed trying to remove the noise cleanly and keep the detail information well [25]−[26].

1.3.2. Existing Gaussian Noise Removal Algorithms

On the other hand, linear filtering algorithms, such as Wiener [27] and Kalman [28] filters are essentially low-pass filters and well-known for their ability to remove the Gaussian noise, but they cannot remove the impulse noise well and tend to blur the fine features in the image. To remove the mixed noise in an image, a combination approach or hybrid filters [29]−[30] have become a promising approach. Garnett et al.

[31] introduced the local image static - Rank-Ordered Absolute Differences (ROAD) to quantify how different in intensity the particular pixels are from their most similar neighbors, and proposed the trilateral filter, modified from the bilateral filter [32], for removing mixed Gaussian and impulse noise.

1.3.3. Existing Neural and Fuzzy Noise Removal Algorithms

Since neural networks have the ability to learn from examples and fuzzy systems have the ability of reasoning to deal with uncertainty, they also have a growing number of applications in image noise removal in the past few years [33]−[40]. Zhang et al. [33] proposed the fuzzy techniques to detect the impulse noise and to find the ultimate remote window to replace the noise-liked pixel with a linear combination of the pixels. Schulte et al. [34] proposed a fuzzy derivative estimation with fuzzy rules in the first stage for noise detection and fuzzy smoothing of neighboring pixels in the second stage. Lee et al. [35] proposed a fuzzy image filter based on genetic learning

process. Russo [36]−[37] proposed a recursive Neuro-Fuzzy filter with specifically designed original multiple output network structure and learned the parameters based on the genetic algorithm. M. E. Yüksel et al. [38] proposed a new impulse noise detector comprises two identical Neuro-Fuzzy subdetectors combined with a decision maker.

On the other hand, there are mainly three kinds of fuzzy approaches used in mixed noise removal of an image. The first kind is the fuzzy weighted average filter [41] and fuzzy weighted median filter [42]. Peng [43]−[44] proposed a multi-level adaptive fuzzy (MLAF) filter, which uses fuzzy sets to adaptively combine simple linear and nonlinear filters to remove varying mixed noise with different levels. The second kind is the fuzzy logic filter, which suggests that individual pixels should not be uniformly fired by each of the fuzzy rules. Choi et al. [47] derived three different filters for each of the three objectives using the fuzzy weighted least squares (FWLS) method. And he defined the criteria for selecting each of the filter based on the local context using the fuzzy rules. Taguchi [48] proposed the modified fuzzy filter (MFF) with new local characteristic calculated with fuzzy rules by using multiple difference values between arbitrary pixels in the filter window. Farbiz et al. [49] proposed the fuzzy logic filter (FLF), which adopted the general structure of fuzzy if-then-else rules mechanism. The S-type fuzzy function enables the non-uniform firing of the basic fuzzy rules. For the third kind uses fuzzy reasoning which is embedded into the neural network structure through genetic learning algorithm. It is able to adapt the filtering action to different distributions of mixed noise.

1.3.4. Existing Human Visual System Algorithms

Many researches have been made on discovering the characteristics of HVS for

years. The characteristics of the HVS have been incorporated into the digital image processing such as watermark encoder design, digital image compression, and image recognition etc [51]−[59]. The perceptual redundancies inherent in a still image are basically due to the inconsistency in sensitivity of the HVS to stimuli of varying levels of contrast and luminance changes in the spatial domain. It was found that the perception of HVS is more sensitive to luminance contrast rather than the uniform brightness [60]. In addition to the magnitude difference between object and the background, different structures of images also cause different visual perceptions for HVS. Many features have been proposed based on the block DCT in frequency domain and Wavelet [61]−[62]. A novel fuzzy decision system inspired by HVS is proposed to classify the image into human perception sensitive and non-sensitive regions.