Abstract: In the process of colorization for black and white movies,the existing automatic colorization models often produce singular result,and the reference-based colorization methods require users to specify reference images,posing a significant challenge in meeting the high requirement of reference images and consuming substantial human efforts.To address this issue,this paper proposes a multi-stage intelligent color restoration algorithm for black-and-white movies (MSICRA).Firstly,the movie is splitted into multiple scene segments by the VGG19 network. Secondly,each scene segment is cut frame by frame,and the edge intensity and grayscale difference of each frame image are used as criteria to assess image clarity,selecting images with clarity ranging from 0.95 to 1 in each scene.Subsequently,we select the first frame that meets the clarity criteria from the filtered images and apply different render factor values to colorize the selected image.We assess the colorization effects using saturation and choose the appropriate render factor for the colorization.Finally,we use the mean squared error between the pre-colorized and post-colorized images to select the best quality colorized image as the reference for the scene segment.Experimental results demonstrate that the proposed algorithm improves the PSNR by 1.32% for the black and white films Lei Feng and 2.15% for The Eternal Wave,and the SSIM by 1.84% and 1.04% respectively.The algorithm not only enables fully automatic colorization but also produces realistic colors that align with human perception.

Key words: Deep learning, Auto-colorization, Scene segmentation, Clarity