在電腦視覺領域中，影像著色一直都因其不確定性而使它成為一個極為困難的問題。所謂不確定性，指的是一個物體的顏色有非常多的可能性，舉例來說，一頂安全帽有可能是黃色，也有可能是藍色，這個特性增加了影像著色中顏色預測的難度。現行的影像著色方法已經可以針對同一物件預測出不同的顏色，提升影像著色的準確度。然而在物件與物件間的交界處，卻時常受到相鄰物件的顏色影響，使不同物件被塗成同一種顏色，產生不自然的著色效果。 本篇論文主要探討在類神經網路的影像著色方法中，結合深度資訊來解決這個問題。近年來，深度學習已成為影像著色方法的主流。另一方面，在許多文獻中，深度資訊已被驗證可以改善電腦視覺以及影像處理相關方法的效能。然而，至今尚未有將深度資訊應用於提升影像著色的方法。因此本篇論文提出一個以類神經網路為基礎，結合深度感知的影像著色方法，並透過許多實驗來驗證提出的方法確實能夠提升影像著色的效能。 Image colorization remains a challenge problem in computer vision. This problem is underconstrained since an object has various colors in the real world. Currently, many studies have achieved good performance in images colorization. However, the color bleeding problem still exists. Different objects share the same color because they are nearby, leading to the result of the boundary between different objects looks unnatural. In this thesis, we study how to combine depth information into neural network-based image colorization. Neural network-based methods are also known as learning-based methods. Recently, learning-based methods have been mainly used in image colorization and have presented promising colorization results. On the other hand, various computer vision works utilizing depth information have achieved good performance. But to the best of our knowledge, depth information was not used in image colorization before. Hence, we propose a depth-aware image colorization network in this thesis. We evaluate the proposed method via several experiments, and the experimental results show that the proposed method can achieve better colorization results.