大部份的消費型數位相機使用單一感光元件，並以色彩濾鏡陣列（Color Filter Array, CFA）覆蓋，因此每個位置只有三原色其中之一的像素值資訊，此類不完整的色彩影像稱為馬賽克影像（Mosaicked Image）；成像時，將馬賽克影像還原成完整彩色影像的方法，稱之為去馬賽克演算法（Demosaicking Algorithms）。我們在本論文提出一基於共同字典學習的去馬賽克演算法，給一馬賽克影像，我們先針對不同的色彩及紋理特徵分類，並學習各自的共同字（Joint Dictionary），再利用共同稀疏表示法（Joint Sparse Representation）來預測真實彩色影像中遺失的色彩資訊。在學習字典及搜尋稀疏表示法的過程中，我們的演算法利用局部限制（Locality Constraint），來找到影像在字典中最有相關性的資訊。實驗結果證明，我們提出的方法在主觀及客觀的比較中，優於現有或最新的方法。

Most digital cameras are overlaid with color filter arrays (CFA) on their electronic sensors, and thus only one particular color value would be captured at every pixel location. When producing the output image, one needs to recover the full color image from such incomplete color samples, and this process is known as demosaicking. In this paper, we propose a novel context-constrained demosaicking algorithm via sparse-representation based joint dictionary learning. Given a single mosaicked image with incomplete color samples, we perform color and texture constrained image segmentation and learn a dictionary with different context categories. A joint sparse representation is employed on different image components for predicting the missing color information in the resulting high-resolution image. During the dictionary learning and sparse coding processes, we advocate a locality constraint in our algorithm, which allows us to locate most relevant image data and thus achieve improved demosaicking performance. Experimental results show that the proposed method outperforms several existing or state-of-the-art techniques in terms of both subjective and objective evaluations.

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