Automatic Video Colorization using 3D Conditional Generative Adversarial Networks
In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. Network convolutional and deconvolutional layers are three-dimensional, with frame height, width and time as the dimensions taken into account. Multiple chrominance estimates per frame are aggregated and combined with available luminance information to recreate a colored sequence. Colorization trials are run succesfully on a dataset of old black-and-white films. The usefulness of our method is also validated with numerical results, computed with a newly proposed metric that measures colorization consistency over a frame sequence.
Code (0)
등록된 구현이 없습니다.
Tasks
ColorizationGenerative Adversarial NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Image Colorization with Generative Adversarial Networks
Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to t…
ColorizationGenerative Adversarial NetworkImage ColorizationLearning Diverse Image Colorization
Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization. Our goal is to model the diversity int…
ColorizationDecoderDiversityGenerative Adversarial Network+1L-C4: Language-Based Video Colorization for Creative and Consistent Color
Automatic video colorization is inherently an ill-posed problem because each monochrome frame has multiple optional color candidates. Previous exemplar-based video colorization methods restrict the user's imagination due…
ColorizationImage ColorizationVCGAN: Video Colorization with Hybrid Generative Adversarial Network
We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning. The VCGAN addresses two prevalent issues in the v…
ColorizationGenerative Adversarial NetworkImage ColorizationVideo Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach
Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication. To efficiently compress raw video data, a colorization-based…
ColorizationDecoderGenerative Adversarial NetworkVideo Compression