Research
A New Method for Impulse Noise Removal in Remote Sensing Images
Existing filtering algorithms use all pixels within a window to filter out the impulse noise. They increase the size of neighboring pixels with the increase of noise density. In this paper, we propose an impulse noise removal algorithm for remote sensing images, that emphasis on few noise-free pixels. The detection map (DM) is constructed from the input noisy image, by assigning a binary value 1 for each corrupted pixel in the input image. By using the detection map, the proposed iterative algorithm searches the noise free pixels with in a small neighborhood. The noisy pixel is then replaced with the median value estimated from noise free pixels. In-order to better appraise the noise cancellation behavior of our filter from the point of view of human perception, we perform segmentation via spline regression on remote sensing image for both noisy image and filtered image. Experimental results show that the filtering performance of the proposed approach is very satisfactory providing better feature extraction in remote sensing images.
A New Method of Image Fusion Technique for Impulse Noise Removal in Digital Images
Image fusion is the process of combining two or more images into a single image while retaining the important features of each image. Multiple image fusion is an important technique used in military, remote sensing and medical applications. This paper presents a new method of image fusion for impulse noise removal in digital images. The images are captured by five sensors and undergo filtering by five different filtering algorithms. These five de-noised images from five different filters are combined into a single image to obtain a high quality image compared to individually de-noised image. The performance of the Image Fusion is evaluated by using a reference image quality metric, Structural similarity Index (SSIM), to estimate how well the important information in the de-noised images is represented by the fused image. Experimental results show that the fused image has more quality than other filtered images.
Segmentation of Microarray Image Using Information Bottleneck
DNA microarrays provide a simple tool to identify andquantify the gene expression for tens of thousands of genessimultaneously. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction.Spots segmentation, which isto distinguish the spot signals from background pixels,is a critical step in microarray image processing. In this paper, new image segmentation algorithm based on the hard version of the information bottleneck method is presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. The input variable here, is the histogram bins and the output variable is the set of regions obtained from the split and merge algorithm. The proposed method is compared with existing segmentation methods such as k-means and Fuzzy C-means. The experimental results show that the proposed algorithm has segmented spots of the microarray image more accurately than other segmentation methods.
