給定一輸入 Bayer 馬賽克影像，解馬賽克是一個重建 RGB 全彩影像的必要流程。然而，Bayer 馬賽克影像的雜訊通常是無法避免的，而且雜訊往往造成重建的 RGB 全彩影像品質下降。本篇論文針對含雜訊的 Bayer 馬賽克影像提出一個新穎且有效的四階段去雜訊優先解馬賽克法進行解馬賽克，使得重建的 RGB 全彩影像有較好的品質。在第一階段，我們提出一個植基於紋理一致的方法從 Bayer 馬賽克影像的虛擬雜訊中區分出真正的雜訊。在第二階段，我們首先提出了一個植基於優先權第一和邊緣感測的恢復方法來估測出真正雜訊的原始綠色像素值。接著，利用內插的紅/藍場引導的恢復方法來估測出缺少的綠色像素值，其中，這些綠色像素值的紅或藍像素值必須是可利用的。剩餘的缺少之綠色像素值則由所提的植基於梯度方向的結合法估測而得。在第三階段，我們提出一個由重建的綠色平面引導之方法來估測真正雜訊的原始紅色和藍色像素值，此外，我們提供一個可允許的雜訊量上界來確保含雜訊之 Bayer 馬賽克影像的完整回復。在第四階段，我們採用一個現存的解馬賽克方法來重建 RGB 全彩影像。根據由兩個典型測試影像圖庫，IMAX 和 Kodak 所產生的含雜訊之 Bayer 馬賽克測試影像集，實驗結果顯示，相較於數個優異的比較方法，所提之去雜訊優先解馬賽克法在重建的 Bayer 馬賽克影像與 RGB 全彩影像有顯著的品質改善。

Given an input Bayer mosaic image, demosaicing is a necessary process to reconstruct the red-green-blue (RGB) full-color image. However, noise is usually unavoidable in the Bayer mosaic image, and it often degrades the quality of the reconstructed RGB full-color image. In this thesis, a novel and efficient four-stage denoising-first demosaicing method is proposed to demosaic the noisy Bayer mosaic image, leading to good quality of the reconstructed RGB full-color image. In the first stage, a texture-consistency based true noise determination approach is proposed to distinguish the true noise from the pseudo-noise in the noisy Bayer mosaic image. In the second stage, first we propose a priority-first- and edge-sensing-based recovery approach to estimate the truly noisy ground-truth G pixels. Then an interpolated R/B field-guided recovery approach is proposed to estimate the missing G pixels, for which the co-located noise-free R/B pixels are available. The remaining missing G pixels are estimated by the proposed gradient orientation-based fusion approach. In the third stage, a reconstructed G color plane-guided approach is proposed to estimate the truly noisy ground-truth R/B pixels, and an upper bound of the allowable noise level is provided to guarantee the complete recovery of the noisy Bayer mosaic image. In the fourth stage, an existing demosaicing method is adopted to reconstruct the RGB full-color image. Based on two typical noisy Bayer mosaic test image sets, which are generated from the IMAX and Kodak test image sets, the experimental results demonstrated that the proposed denoising-first demosaicing method achieves substantial quality improvement of the reconstructed Bayer mosaic and RGB full-color images when compared with several state-of-the-art comparative methods.

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