Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.

A color transfer algorithm between images based on two-stage convolutional neural network (CNN) is proposed. The first stage network is based on VGG19 architecture as the backbone. The reference image-based color transfer (RICT) model was used to extract the features of the reference image and the target image, so as to realize the color transfer between them. The second stage is based on progressive convolutional neural network (PCNN) as its backbone. The palette-based emotional color enhancement (PECE) model is adopted to enhance the emotional coloring of the resulting image by comparing the palette, emotional value and the proportion of each color of the reference image. Through five sets of experiments, it is proved that the visual effect processed by our model is obviously better than several main colorization methods in both subjective evaluation and objective data. It can be applied to various complex scenes, and in the near future, it can also be better applied to the fields of digital restoration of old image archives, medical image coloring, art restoration, remote sensing image enhancement, infrared image enhancement and other fields.

With the changes of eras, traditional movies may suffer continuous frame damage after digits due to improper preservation. In order to solve this problem, we proposed a new inpainting technique for continuous video sequences based on deep neural networks. We introduced the latest image restoration techniques for inpainting key frames of new scenes. Then we use the deep neural network interpolation algorithm to interpolate the intermediate frames, so that the video can achieve a coherent effect in time. In order to preserve the original information as much as possible, we only replace the damaged areas and preserve most of undamaged areas, and finally perform image blending. We test different types of videos and compare them with other methods. Our method has higher quality video inpainting than existing methods.

Due to the underdeveloped scanning technology, some old movie films are scanned in digital format with lower resolution, which does not meet the viewing needs of contemporary viewers. Therefore, it is necessary to superresolution processing them to improve the image quality. However, some old movies will appear blurred after scanning. In this case, the existing algorithm super-resolution reconstruction results are often not ideal. This paper adds image deblurring pre-processing before the super-resolution processing. First, the old movie is deblurred according to the deblurring generation training model against the network, and then the image is super-resolution processed by the sub-pixel convolution network. The method aims to improve the problem that the repair effect caused by the image blur caused by the old film in the super-resolution reconstruction is not ideal.

In old movies, the common jitter is caused by translation, rotation and zooming. Aiming at the common phenomenon of video jitter, this paper proposes a method of combining Lucas-Kanade sparse optical flow with feature point matching to estimate the global motion parameters. Then, it is applied to the restoration of old film, so as to realize the motion compensation from the jitter frame to the reference frame, so as to achieve the image stabilization effect of the continuous sequence frame of the old movie. Experiment’s results show that this algorithm has a good real-time performance, and it can solve the problem of smooth transition between frame and frame effectively.