Hybridization of Otsu Method and Median Filter for Color Image Segmentation

In this article a novel algorithm for color image segmentation has been developed. The proposed algorithm based on combining two existing methods in such a novel way to obtain a significant method to partition the color image into significant regions. On the first phase, the traditional Otsu method for gray channel image segmentation were applied for each of the R,G, and B channels separately to determine the suitable automatic threshold for each channel. After that, the new modified channels are integrated again to formulate a new color image. The resulted image suffers from some kind of distortion. To get rid of this distortion, the second phase is arise which is the median filter to smooth the image and increase the segmented regions. This process looks very significant by the ocular eye. Experimental results were presented on a variety of test images to support the proposed algorithm.

Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise

Image denoising is a critical issue in the field of digital image processing. This paper proposes a novel Salt & Pepper noise suppression by developing a Kriging Interpolation Filter (KIF) for image denoising. Gray-level images degraded with Salt & Pepper noise have been considered. A sequential search for noise detection was made using kXk window size to determine non-noisy pixels only. The non-noisy pixels are passed into Kriging interpolation method to predict their absent neighbor pixels that were noisy pixels at the first phase. The utilization of Kriging interpolation filter proves that it is very impressive to suppress high noise density. It has been found that Kriging Interpolation filter achieves noise reduction without loss of edges and detailed information. Comparisons with existing algorithms are done using quality metrics like PSNR and MSE to assess the proposed filter.

Hybrid Image Segmentation using Discerner Cluster in FCM and Histogram Thresholding

Image thresholding has played an important role in image segmentation. This paper presents a hybrid approach for image segmentation based on the thresholding by fuzzy c-means (THFCM) algorithm for image segmentation. The goal of the proposed approach is to find a discerner cluster able to find an automatic threshold. The algorithm is formulated by applying the standard FCM clustering algorithm to the frequencies (y-values) on the smoothed histogram. Hence, the frequencies of an image can be used instead of the conventional whole data of image. The cluster that has the highest peak which represents the maximum frequency in the image histogram will play as an excellent role in determining a discerner cluster to the grey level image. Then, the pixels belong to the discerner cluster represent an object in the gray level histogram while the other clusters represent a background. Experimental results with standard test images have been obtained through the proposed approach (THFCM).

Image Interpolation Using Kriging Technique for Spatial Data

Image interpolation has been used spaciously by customary interpolation techniques. Recently, Kriging technique has been widely implemented in simulation area and geostatistics for prediction. In this article, Kriging technique was used instead of the classical interpolation methods to predict the unknown points in the digital image array. The efficiency of the proposed technique was proven using the PSNR and compared with the traditional interpolation techniques. The results showed that Kriging technique is almost accurate as cubic interpolation and in some images Kriging has higher accuracy. A miscellaneous test images have been used to consolidate the proposed technique.

Image Denoising Using Interquartile Range Filter with Local Averaging

Image denoising is one of the fundamental problems in image processing. In this paper, a novel approach to suppress noise from the image is conducted by applying the interquartile range (IQR) which is one of the statistical methods used to detect outlier effect from a dataset. A window of size kXk was implemented to support IQR filter. Each pixel outside the IQR range of the kXk window is treated as noisy pixel. The estimation of the noisy pixels was obtained by local averaging. The essential advantage of applying IQR filter is to preserve edge sharpness better of the original image. A variety of test images have been used to support the proposed filter and PSNR was calculated and compared with median filter. The experimental results on standard test images demonstrate this filter is simpler and better performing than median filter.