计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
2期
233-236
,共4页
图像处理%压缩感知%稀疏表示%阈值处理%信号重构%Curvelet 变换
圖像處理%壓縮感知%稀疏錶示%閾值處理%信號重構%Curvelet 變換
도상처리%압축감지%희소표시%역치처리%신호중구%Curvelet 변환
image processing%compressed sensing%sparse representation%threshold processing%signal reconstruction%Curvelet transform
压缩感知主要采用离散余弦变换(DCT)和正交小波进行图像的稀疏表示，但是 DCT 时频分析性能不佳，小波方向选择性差，不能很好地表示图像边缘的信息。为此，利用 Curvelet 变换具有的多尺度、各向奇异性、更高稀疏表示性能等特性，提出基于 Curvelet 变换的图像压缩感知重构算法，采用 Curvelet 对图像进行稀疏表示和小波域阈值处理，以此解决信号重构噪声问题。实验结果证明，与传统小波变换和 Contourlet 变换相比，该算法在 Lena 图像上峰值信噪比平均提高了1.86 dB 和1.15 dB。将 Curvelet变换应用于压缩感知，能使图像边缘和平滑部分得到最优的表示，图像细节部分重构效果得到大幅提升，有效提高图像整体重构质量。
壓縮感知主要採用離散餘絃變換(DCT)和正交小波進行圖像的稀疏錶示，但是 DCT 時頻分析性能不佳，小波方嚮選擇性差，不能很好地錶示圖像邊緣的信息。為此，利用 Curvelet 變換具有的多呎度、各嚮奇異性、更高稀疏錶示性能等特性，提齣基于 Curvelet 變換的圖像壓縮感知重構算法，採用 Curvelet 對圖像進行稀疏錶示和小波域閾值處理，以此解決信號重構譟聲問題。實驗結果證明，與傳統小波變換和 Contourlet 變換相比，該算法在 Lena 圖像上峰值信譟比平均提高瞭1.86 dB 和1.15 dB。將 Curvelet變換應用于壓縮感知，能使圖像邊緣和平滑部分得到最優的錶示，圖像細節部分重構效果得到大幅提升，有效提高圖像整體重構質量。
압축감지주요채용리산여현변환(DCT)화정교소파진행도상적희소표시，단시 DCT 시빈분석성능불가，소파방향선택성차，불능흔호지표시도상변연적신식。위차，이용 Curvelet 변환구유적다척도、각향기이성、경고희소표시성능등특성，제출기우 Curvelet 변환적도상압축감지중구산법，채용 Curvelet 대도상진행희소표시화소파역역치처리，이차해결신호중구조성문제。실험결과증명，여전통소파변환화 Contourlet 변환상비，해산법재 Lena 도상상봉치신조비평균제고료1.86 dB 화1.15 dB。장 Curvelet변환응용우압축감지，능사도상변연화평활부분득도최우적표시，도상세절부분중구효과득도대폭제승，유효제고도상정체중구질량。
Discrete Cosine Transform(DCT) and wavelet transform are used for sparse representation, but DCT can’t analyse well in domain of time and frequency. The directional selectivity of wavelet transform is poor and can’t reconstruct edge information well enough. Against the optimization of sparse representation, Curvelet transform has characters of multi-scale, singularity and more sparsity. This paper proposes a compressed sensing reconstruction algorithm based on Curvelet transform, which uses Curvelet transform for sparse representation and thresholding method in wavelet domain to solve the noise problem of signal reconstruction. Results demonstrate that the algorithm gets 1.86 dB higher Peak Signal to Noise Ratio(PSNR) and 1.15 dB higher PSNR compared with traditional wavelet transform and Contourlet transform. As Curvelet transform is applied to compressed sensing, optimal result of edge and smooth part of image are got, also the reconstructed quality of details is increased.
