基于非局部稀疏編碼的超分辨率圖像復(fù)原
doi: 10.11999/JEIT140481 cstr: 32379.14.JEIT140481
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61070138)資助課題
Super-resolution Image Restoration Based on Nonlocal Sparse Coding
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摘要: 基于壓縮感知的超分辨率圖像復(fù)原方法通常采用局部稀疏編碼策略，對(duì)每一圖像塊獨(dú)立編碼，易產(chǎn)生人工的分塊效應(yīng)。針對(duì)上述問(wèn)題，該文提出一種基于非局部稀疏編碼的超分辨率圖像復(fù)原方法。該算法在字典訓(xùn)練和圖像編碼過(guò)程中分別運(yùn)用圖像的非局部自相似先驗(yàn)知識(shí)，即利用低分辨率圖像的插值圖像訓(xùn)練字典，并通過(guò)計(jì)算相似塊局部編碼的加權(quán)平均，得到每一圖像塊的非局部稀疏編碼。仿真實(shí)驗(yàn)表明，所提算法能夠獲得更優(yōu)的復(fù)原效果，并且對(duì)于含噪圖像具有較強(qiáng)的魯棒性。
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關(guān)鍵詞:
- 超分辨率圖像復(fù)原 /
- 壓縮感知 /
- 非局部自相似 /
- 非局部稀疏編碼 /
- 單字典訓(xùn)練
Abstract: Super-resolution image restoration methods based on Compressive Sensing (CS) generally adopt local sparse coding strategy. Such strategy encodes each image block independently, which easily induces artificial blocking effect. To overcome this problem, a super-resolution image restoration method based on nonlocal sparse coding is proposed. The nonlocal self-similarity of image is considered as a prior in the dictionary training and image coding processes, respectively. Specifically, the proposed algorithm trains the dictionary with interpolated low-resolution images, and calculates the weighted average local code of similar patches, in order to obtain the nonlocal sparse code of each image block. Numerical experiments suggest that the proposed algorithm has a good recovery performance, and is robust to image noise. -
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