| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 98 === This thesis presents a novel impulse noise removal approach to improve the quality of restored image. Presently, noise removal approaches generally consist of two stages: noise detection and noise replacement. In the stage of noise detection, a noisy pixel is identified. If a noisy pixel is detected, a noise replacement scheme is applied to replace the noisy pixel with un-noisy one. When the pixel is uncorrupted, then leave it intact. A well-known noise detection scheme is the boundary discriminative noise detection (BDND). The performance of BDND is heavily dependent on the accuracy of boundary detection. When the boundaries are not determined appropriately, then the noisy pixel will be shown in the restored image. To improve the detection performance of BDND, two modified BDND are proposed in this thesis. They are called MBDND_1 and MBDND_2, respectively. In the MBDND_1, a modification is made on the inequalities of BDND while a boundary resetting scheme is applied in the MBDND_2. Besides, a novel noise detection scheme called the noise detection based on estimated noise distribution (NDEND) is presented and shown having much better detection performance than the BDND, the MBDND_1, and the MBDND_2.
As for the noise replacement, a class of adaptive neighborhood median filters (ANMF) is introduced. Note that the window size used in the filtering process has smoothing effect on the restored image. That is, larger windows applied in the filtering process result in a stronger smoothing effect on the restored image. Thus, the proposed ANMF employs smaller windows, when replacing noisy pixels, for better visual quality of a restored image, especially in high noise density cases. To justify the proposed NDEND and ANFM, several images are given where the salt and pepper noise, the random-valued noise, and the unbalanced density noise, with various densities are under study. Besides, the results obtained from the NDEND/ANFM are compared with the well-known boundary based approach BDND. It indicates that the proposed NDEND/ANFM generally has better performance than that in the BDND both in objective and/or subjective assessments.
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