Research On Denoising And Superpixels Generation Methods In Image Processing

Visual is an important way to communicate information to human.Image process-ing aims to obtain information from the visual data,such as images.Image processing has a wide range of applications,such as agriculture,oil exploration,bio-medicine.Im-age denoising and Superpixel generation(over-segmentation)as the two fundamental problems in image processing field,provide a solid basis for image analysis and under-standing.In the stage of acquisition,compression and transmission,the image may be dis-turbed by the noise inevitably.As a result,the image distortion would interfere with the performance of the following tasks in image processing by reducing the accuracy of the information and decisions.Image denoising is to remove noise from the distorted images.Due to the diversity of noise,there are many challenges in the research on de-noising methods.On one hand,most existing image denoising algorithms assume that the data is corrupted by i.i.d.Gaussian white noise.As a result,they do not work well when other types of noise is present,such as salt and pepper noise.The reason is that the performance of a denoising method highly depends on how well the noise in the data fits the noise model assumed in designing the method.On the other hand,image denoising methods can be applied on video sequences directly by processing each of the frames separately.However,video sequences usually have very high temporal redun-dancy.That means neighboring frames have high similarity in structure,which should be effectively used for better performance.Superpixels group pixels with similar properties into perceptually meaningful atom-ic regions that can be used to replace the rigid structure of the pixel grid in images.In this way,image primitives and redundancy can be reduced greatly.Furthermore,for subsequent applications,it is more convenient and effective to find image features based on the regions than pixels.In general,superpixel segmentation algorithms are usually employed as preprocessing steps of many computer vision tasks to improve their per-formances.For the variety types of images and its own complexity,there are many challenges in the research on superpixels generation methods.On one hand,for the negative correlation between the boundary adherence and the compactness of superpix-els,it is difficult to find an algorithm that can generate superpixels with high boundary adherence and compactness simultaneously.On the other hand,most existing meth-ods try to generate superpixels of the same sizes.However,in the common sense,a small number of elements are enough to depict an object with few features and more elements are required to represent an object with many details.To represent an image with superpixels as few as possible,an adaptive superpixel segmentation algorithm that is sensitive to features is urgently needed.This thesis focuses on the problems of image denoising and superpixel generation methods and proposes some solutions.The major works are listed as follows:1.We proposed a method based on low-rank matrix recovery to remove salt and pepper noise in video.Unlike single image denoising techniques,noise removal from video sequences aims to utilize both temporal and spatial information.Based on the idea of non-local method,by grouping neighboring frames based on similarities of the whole images in the temporal domain,the problem of removing salt and pepper noise from a video tracking sequence can be formulated as a low-rank matrix recovery problem.The proposed method can not only remove noise effectively,but also preserve edges and details.The performance of our proposed approach compares favorably to that of existing algorithms.2.We proposed a simple algorithm to generate superpixels with boundary con-straint.A novel distance function is designed to balance among boundary adherence,intensity homogeneity and compactness characteristics of the resulting superpixels.In order to evaluate whether a pixel was on the boundaries of an image,we calculated the probabilities based on its neighbours.We updated the positions and intensities of super-pixel seeds based on the three-sigma rule and the processing was more accurate.The experimental results demonstrate that our algorithm is more effective and accurate than previous superpixel methods and achieves a comparable tradeoff between the boundary adherence and compactness of superpixels.3.We proposed an automatic superpixel generation algorithm based on quadric error metric(QEM)in 3D space.Digital images are discrete and got by sampling from a continuous surface of a 3D scene.That means image processing can be done in three dimensions.We took an image as a triangle mesh and generated superpixels by mesh simplification.We proposed a modified quadric error metric method to deal with 3D meshes of images by redefining the cost of edge collapse and termination conditions of simplification.At the same time,we selected more features of images as attributes of vertexes.The experimental results demonstrate that this method is effect to generate superpixels with varying size and the boundaries of superpixels adhere to the boundaries of images well.