The Application of a Marvel Technology to the Sidescan Sonar Image Denoising by Fractal-Wavelet Denoising Based on Self-Similarity

Abstract

[[abstract]]側掃聲納影像通常會受到雜訊干擾，因此使用適當的方法消除雜訊是並要的，然而 對影像盡可能地去進行降低或消除雜訊，並且同時盡可能地保留或不破壞影像紋理是一 個很困難的問題。 本計畫將應用目前在聲納影像去雜訊中很普遍的小波變換方法，並結合碎形影像壓 縮技術，稱為碎形小波編碼(fractal-wavelet coding)，提出一個基於特徵相似的去除雜訊 的新方法。由於自我相似的是碎形理論中最重要的屬性，而影像經由小波轉換後的各個 小波 subband(次頻帶)間也存在著自我相似性，因此本計劃基於紋理特徵的相似程度，以 具有相似的紋理的一部份影像去近似部份 noise-free 圖像時，由於雜訊不具有自我相似， 因此雜訊在此過程中會被降低或消除。 本計畫所提出的紋理分析方法是關注在小波轉換後的資料的灰階值分佈，根據[Tin et al., 2011]提出的粗糙度的碎形維度(REFD)用來測量表面紋理的相似程度。因此，本研 究使用粗糙度的碎形維數測量子帶之間的紋理的相似性，並利用碎形演算法將相鄰的子 帶按其紋理的相似性進行編碼。 本計畫預計提出的方法描述如下：首先影像經由小波轉換得到 multi-wavelet 次頻 帶，測量子帶 subblock 的粗糙度的碎形維度值，以最小粗糙度的碎形維度差值找出最佳 匹配的 range subtree 和 domain subtree。接者，對最佳匹配的 domain subtree 執行仿射轉 換, 並記錄下這個 domain subtree 以及最佳的仿射轉換即為編碼。解碼即為執行 inverse wavelet transform 的 affine transformation，此過程即在近似一個 noise free image。本計畫 使用由寶拉麗絲號(Polaris) 所提供的側掃聲納圖像做為實驗標的，本研究成果預期可以 有效的降低或消除雜訊，並且不減損影像視覺品質(visual quality)。 本計劃將以 1 年的時間完成如下之工作: 1) 建立側掃聲納影像降低雜訊之相關理 論及演算法。2)以 Matlab 開發程式系統。3) 使用台灣的寶拉麗絲號所取得的側掃聲納影 像做為本計劃之驗證及分析之實驗標的。4) 參加國際水下技術相關研討會，並提出研究 報告。 Side-scan signal collected from seabed is primarily reflected from many elements of bottom roughness add to which retains a variation with time and which relates to the texture of the bottom. Image denoising is a difficult problem which is how to remove the noise as much as possible while detract image texture information as little as possible. Some of the efficient noise-reducing methods are reported using fractal block coding method combined with wavelet image coding method. This project proposes a fractal-wavelet denoising alternative on the basis of the introduced texture analysis technique. Texture has been regarded as a similarity grouping in an image and roughness is a perceived property to describe the structural texture. Fractal dimension (FD) is used to measure the degree of complexity of the surface texture. This project uses the roughness FD (REFD) suggested by Tin et al. to examine the texture similarity of an image. The REFD is introduced in fractal-wavelet coding process in finding each range subtree for its optimal matching domain subtree according to the best possible accuracy of texture similarity measures. It is believed that such measurement would well capture the texture similarity. To denoise a sonar image by the proposed algorithm, the image is transformed into multi-wavelet domain. Then apply fractal block coding to find each range subtree the best matched domain subtree, such that the differential between the two roughness FD values of domain-range subtrees in correspondent subbands is the minimal. Therefore the encoding is taken to store the position of the best matched domain subtree and the affine transformation in code book. The decode image is the reverse of encoding according to the code book and results the approximate image. We are going to test our proposed algorithm with the side-scan sonar images which is the wreck of M.V. Sea Angel taken by the Polaris, Taiwan. The experiment will investigate the corresponding quality of the images using two error criteria: mean square error (MSE) and the peak signal to noise ratio (PSNR). The experimental results will indicate that the appropriation of the REFD as the criteria of range-domain matching in fractal-wavelet coder to well approximate the experimental images. We will see that the proposed REFD FW scheme is adaptable in denoising side-scan sonar image and that the images are more appealing visually. We will complete the following tasks in project period of one year: 1) apply the relative theories of denoise to develop our proposed algorithm. 2) implement the algorithm by developing the software system in Matlab. 3) apply the system to the side-scan sonar images from the Polaris for testing the algorithm. 4) attend and publish the result to a international under-water conference