IMAGE DENOSING USING NEAREST NEIGHBOUR THRESHOLDING METHOD
Navdeep Kaur, Kuldeep Sharma · 2013
DENOISING is the technique used to retrieve the image from the noisy image environment. It uses the visual content of images like color, texture, and shape as the image index to retrieve the images from the image pixels. The purpose of the work is to retrieve images with less noise .Images with noise in the database can be reduced with different techniques including the denoising method and the detection and removal of the existing parameters to detect the new proposed project. DENOISING algorithms is still limited and often worse than keyword based approaches. The problem stems from the fact that visual similarity measures, such as color histograms, in general do not necessarily match perceptional semantics and subjectivity of images. In addition, each type of image features tends to capture only one of many aspects of image similarity and it is difficult to require a user to specify clearly which aspect exactly or what combination of these aspects he/she wants to apply in defining a query. To address these problems, we propose the utilization of the Scale Invariant Feature Transform algorithm. SIFT has been demonstrated to be very suitable for object detection in images with high resolution. However, SIFT performs poorly when it is faced with images of poor resolution. Matching features across different images in a common problem in DENOISING. SIFT extracts distinctive invariant features from an image which can be used for performing reliable matching between different views of an object or scene. These features are scale and rotation invariant and have shown to provide robust matching across many different affine distortions, changes in 3D viewpoints, addition of noise and change in illumination. A single feature can be matched correctly with a very high probability against a large database of features of many images. The Adaptive Threshold module is used in uneven lighting conditions when you need to segment a lighter foreground object from its background. In many lighting situations shadows or dimming of light cause thresholding problems as traditional thresholding considers the entire image brightness. Adaptive Thresholding will perform binary thresholding by analyzing each pixel with respect to its local neighborhood. A general systematic method for the detection and segmentation of targets is developed. We use the term bright to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive