WEKO3
アイテム
Image Restoration with Multiple Directional Transforms
http://hdl.handle.net/10191/35579
http://hdl.handle.net/10191/35579119a3b63-be2e-4448-88aa-9b8120138a00
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (5.5 MB)
|
|
|
|
要旨 (181.4 kB)
|
|
| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2015-12-14 | |||||
| タイトル | ||||||
| タイトル | Image Restoration with Multiple Directional Transforms | |||||
| タイトル | ||||||
| タイトル | Image Restoration with Multiple Directional Transforms | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Poisson noise | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Multiple directional transform | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Image fusion | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Luminance contrast | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Interscale relation | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| その他のタイトル | ||||||
| その他のタイトル | 複数指向性変換を利用した画像復元 | |||||
| 著者 |
Chen, Zhiyu
× Chen, Zhiyu |
|||||
| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 50686 | |||||
| 姓名 | 陳, 智雨 | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This thesis deals with the application of multiple directional transforms to image restoration, and discusses two cases of image restoration, image denoising and image fusion. In Chapter 1, the background of image restoration is described. First, image denoising problems are addressed. Image denoising is a principal problem of image processing and the purpose is to obtain an original picture as an ideal one. For photon acquisition systems, low-light image denoising is becoming in use in optical imaging applications such as astronomical imaging, fluorescence microscopy appliances, magnetic resonance imaging. In this case, the noises are strongly dependent on the signals and approximately obeys Poisson distribution, which leads difficulties in denoising process. The denoising problem for Poisson noise can be modeled by a modular fashion through variance stabilization. Using the variance stabilization, denoising techniques for additive Gaussian noises become available for Poisson denoising. This chapter summarizes some of such existing methods. Next, image fusion problems are dealt with. Image fusion is a technique to synthesize a full focused picture, in which all the contents are focused, from a set of partially focused images with different focal lengths. At present, there are several types of image fusion techniques. Those include spatial domain, feature space and transform domain techniques. In the spatial domain and feature space approaches, the synthesis performance heavily depends on the adopted segmentation algorithm, and they prone to fail fusion at object edges, while the transform domain approach is influenced by the adopted transform. In order to improve the quality of fused image, some disadvantages of existing methods are discussed in this chapter. This chapter mentions the possibility of improving the performance of Poisson denoising and image fusion from viewpoints different form existing researches. In Chapter 2, from comparison with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), the features of directional lapped orthogonal transforms (DirLOTs) are discussed and emphasized For preparation of Chapter 3 and later, explanations on DirLOTs are given through some figures and expressions. Based on the relationship between directivity of DirLOT and slant edge and texture of image, the possibility of the performance improvement of image restoration is described. | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In Chapter 3, a Poisson denoising method is proposed. Various discrete wavelet transforms have been used for Poisson image denoising. However, the transforms have disadvantages such as shift variance, aliasing, and lack of directional selectivity. PURE-LET is known as an efficient Poisson denoising technique. However, PURE-LET has a disadvantage of representing slant geometric structures. As another method, there is a technique to convert Poisson noise to additive Gaussian noise so that typical Gaussian denoising technique becomes available. Among many Gaussian denoising methods, the SURE-LET approach is known to be relatively efficient. SURE-LET is a kind of soft-thresholding method that uses a relation among inter-scale coefficients of an orthonormal wavelets. Its performance, however, depends on the adopted wavelets. Classical separable transforms bring poor representation of slant edges and textures. In order to solve this problem, this section proposes to combine the variance stabilizing transformation (VST), SURE-LET approach and multiple DirLOTs (M-DirLOTs). Experimental results show that the proposed method is able to significantly improve the denoising performance. In Chapter 4, an image fusion method is proposed. Image fusion is a scheme to improve the quality of information from multiple images. This chapter deals with an image fusion technique in wavelet transform domain. Some wavelet-based algorithms were developed. For more effective representation of image, we use multiple directional transforms to fuse images. M-DirLOTs can overcome a disadvantage of traditional separable wavelets for representing slant textures and edges of images. This work analyses characteristics of local luminance contrast and suggests a novel fusion rule based on inter-scale relation of wavelet coefficients. Relying on the above consideration, a novel image fusion method based on inter-scale relation in M-DirLOTs domain is proposed. Some experimental results show that the proposed method improves the fusion performance. In Chapter 5, conclusions of this thesis and future works are summarized. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 学位の種類: 博士（工学）. 報告番号: 甲第4085号. 学位記番号: 新大院博（工）甲第436号. 学位授与年月日: 平成27年9月24日 | |||||
| 書誌情報 | p. 1-51, 発行日 2015-09-24 | |||||
| 出版者 | ||||||
| 出版者 | 新潟大学 | |||||
| 著者版フラグ | ||||||
| 値 | ETD | |||||
| 学位名 | ||||||
| 学位名 | 博士（工学） | |||||
| 学位授与機関 | ||||||
| 学位授与機関名 | 新潟大学 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2015-09-24 | |||||
| 学位授与番号 | ||||||
| 学位授与番号 | 13101甲第4085号 | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 新大院博（工）甲第436号 | |||||
