Research On Gray Correction And Segmentation Technology For Un-uniform Illumination Image

Image segmentation is one of the most challenging tasks in image analysis and pattern recognition. The importance of segmentation result has a direct impact on the effectiveness of the continuous task, and it plays a vital role in image processing.While in the processing of image acquisition, the pixel gray is inhomogeneous in image because the nonuniform illumination surroundings or under exposure lead to the whole pixel gray lower than the actual value in nightly image and infrared image. The present uneven illumination makes the image segmentation and later processing more difficult because it deteriorated the real image badly. Therefore, it is urgent to pre-process the uneven illumination image before segmentation.The thesis presents some new reference methods and some revised strategies in view of the serious shortcomings existing in present image gray correct and segmentation. The main achievements are stated as follows:1.A novel pixel gray stretch in wavelet domain and rapid image threshold segmentation algorithm is proposed in view of the case that there is no visible double peak histogram in uneven illumination image. Firstly,it stretch the image gray and enhance the image contrast by the Otsu threshold segmentation method and Retinex model in wavelet domain. Secondly,since 2-D threshold segmentation methods have some faults such as supposing partial region close to zero and utilizing shannon entropy as the optimization function which has extensive property, so it is necessary to improve the gray-neighbor gray histogram to gray-gradient histogram and to cluster the new histogram field for reducing the data size. At last, take the Tsallis cross entropy as the optimization function and calculate the optimum segmentation threshold by particle swarm optimization algorithm.2.In the modeling analysis and pixel gray correction of gray inhomogeneous in image, a intensity correction algorithm adaptively based on energy minimization is supposed by means of studying the presentation model of uneven illumination and adopting the Retinex model and the mathematical idea that the uneven illumination can be presented with the linear combination of some basis functions. The algorithm build a novel model to describe uneven illumination field in any image and their parameters are computed rapidly by energy minimization, so it can be used in pre-processing effectively before image segmentation.3.An illumination robust PCNN image segmentation algorithm is proposed. Firstly, the image intensity inhomogeneous is corrected by the uneven illumination correction method based on energy minimization and the idea that the continuous curve can be denoted by linear combination of some basis functions. Secondly, some parameters can be determined adaptively; the parameterβis set by the contrast of pixels through human visual system; the link matrix W is set by the distance and gray difference of neighbor pixels. At last, the regional mutual entropy is adopted to stop iteration because some original forms of entropy do not take into account the structural information in image.4.A robust image segmentation algorithm based on fuzzy C-means clustering is proposed which can estimate the intensity inhomogeneous simultaneously and free from the influence of uneven illumination. It introduce the model of presentation uneven intensity inhomogeneous with the linear combination of basis functions into objective function in FCM in view of the fuzziness and intensity inhomogeneous in microscopic image segmentation. As result, it obtains the segmentation results and estimates the uneven illumination fields simultaneously by means of iteration method. Meanwhile, the algorithm relieves the influence produced by both normal noise and background noise.5.An illumination robust color cell image segmentation algorithm is proposed against the influence of uneven illumination by means of the principal component analysis which can transform space and lessen dimension for multi-dimensional data effectively. Firstly, transform the color image data by principal component analysis to select first or first two components according to their own degree of contribution and segment them, then compose the segmentation results to last result. Segment the first component image by energy minimize based segmentation algorithm which can estimate the intensity inhomogeneous simultaneously,for the second component image,segment it by improved PCNN method. finally compose the two segmentation results according to their degree of contribution respectively.