(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 12, 2017 454 | Page www.ijacsa.thesai.org Image noise Detection and Removal based on Enhanced GridLOF Algorithm Ahmed M. Elmogy Prince Sattam Bin AbdelAziz Univ, KSA Computers & Control Eng. Dep., Tanta Univ., Egypt Eslam Mahmoud Computers & Control Eng. Dep., Tanta Univ., Egypt Fahd A. Turki Electrical Engineering Dept., King Saud Univ., KSA Abstract—Image noise removal is a major task in image processing where noise can harness any information inferred from the image especially when the noise level is high. Although there exists many outlier detection approaches used for this task, more enhancements are needed to achieve better performance specifically in terms of time. This paper proposes a new algorithm to detect and remove noise from images depending on an enhanced version of GridLOF algorithm. The enhancement aims to reduce the time and complexity of the algorithm while attaining comparable accuracy. Simulation results on a set of different images proved that proposed algorithm achieves the standard accuracy. Keywords—Outlier detection; image noise removal; LOF; GridLOF I. INTRODUCTION Image noise removal is one of the low level image processing operations with efficient noise removal is defined as the first step in image processing applications as all tasks are dependent on the efficiency of the noise removal [1], [2]. However, this is a very challenging task as noise removal algorithms should preserve useful information in the image while removing noise. Outlier detection is the process of identifying data items or points that do not agree with an expected pattern or other items in a dataset (outliers) [3]. The importance of outlier detection derives from the fact that the deduced data might be translated into actionable information. This actionable information can be used in many applications. These applications include but not restricted to fraud detection for credit cards [4], control systems [5], medical research, parallel software applications [6], steganalysis in image sharing applications [7], intelligent transportation system, wireless sensor networks[8, 9], and human skin detection[10]. Outlier detection methods can be classified into three categories [3], [11], [12]: statistical methods, proximity-based methods, and clustering-based methods. There are two types of proximity-based outlier detection methods: distance-based and density-based methods. The local outlier factor (LOF) is considered as the most common density-based outlier detection [13], [14]. LOF as proposed in [15] focuses on the relative density of a data item against its neighbors. For each data item, relative density is used to calculate probability of being an outlier which called the local outlier factor (LOF). Although there are many research efforts in the literature on simplifying and enhancing the LOF algorithm [16]-[18], more enhancements need to be done to deal with big data. LOF', LOF", and Grid LOF [19], enhanced GridLOF [20] and FastLOF [21] are examples of these efforts. Many researches [4], [6], [7] are interested in detecting noise in the image with the aim to save image’s useful information. According to this, the noise in the image (outlier) is determined before applying the filter to these pixels (i.e. outliers) only. Thus, the image's useful information is not harmed. This paper proposes a new noise detection and removal algorithm to eliminate noise from images using an enhancement version of GridLOF introduced in [20]. The proposed algorithm is able to detect the noise correctly with better accuracy than GridLOF and in lower time and complexity. The rest of the paper is organized as follows. Section 2 provides the related work about image filters. Details of our algorithm are introduced in Section 3. Section 4 describes the simulation results and provides an analytical discussion on the quality of our proposed method. Finally, Section 5 introduces conclusions and future work. II. RELATED WORK Most research working on removing noise from images depends on filters, such as standard median filter [22], [23], weighted median filter [23]-[25] and adaptive median filter [23], [26]. Some research worked based on modern methods such as non-LocalMean based methods [27], [28], PDE based methods [29], [30]. All of these filters change the values of both noisy and non-noisy pixels. Thus, researchers started to depend on fuzzy notion such as the work introduced in [31] which proposed an algorithm to remove noise depending on a fuzzy impulse detection technique with a better ability to detect noisy pixels without previous training. Also, in [32] a neuro- fuzzy operator is proposed based on two adaptive NF filter with a post-processor. Normal filters use the same filter for all pixels in the image which not only fixes the noisy pixels, but also it distorts the right pixels. Towards enhancing LOF algorithm, many efforts have been seen in the literature. Kernel Density-Based Local Outlier Factor (KLOF) is an outlier detection algorithm which is based on LOF [33]. In [34], a hierarchical framework approximated LOF is used for effective outlier detection. Also, an enhanced