Papers Image Colorization
“Image Colorization” 태그가 달린 논문 138편 · 필터 해제
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However,…
Image ColorizationImage Super-ResolutionSSIMSuper-ResolutionGuiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at …
ColorizationImage ColorizationSketch ColorizationCT2: Colorization Transformer via Color Tokens
Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation. In this paper, we propose an…
ColorizationImage ColorizationPalGAN: Image Colorization with Palette Generative Adversarial Networks
Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. T…
ColorizationImage ColorizationExemplar-Based Image Colorization with A Learning Framework
Image learning and colorization are hot spots in multimedia domain. Inspired by the learning capability of humans, in this paper, we propose an automatic colorization method with a learning framework. This method can be …
ColorizationImage ColorizationParaColorizer: Realistic Image Colorization using Parallel Generative Networks
Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learn…
ColorizationImage ColorizationMemory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization
Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans an…
ColorizationGenerative Adversarial NetworkImage ColorizationImage-to-Image Translation+3TIC: Text-Guided Image Colorization
Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make…
ColorizationImage ColorizationGLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
We show that pre-trained Generative Adversarial Networks (GANs) such as StyleGAN and BigGAN can be used as a latent bank to improve the performance of image super-resolution. While most existing perceptual-oriented appro…
ColorizationDecoderImage ColorizationImage Restoration+2iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer
Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-pr…
ColorizationDecoderImage ColorizationPoint-interactive Image ColorizationImage Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images
We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. M…
ColorizationImage ColorizationUViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications…
ColorizationDepth EstimationDepth PredictionImage Colorization+3Influence of Color Spaces for Deep Learning Image Colorization
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different colo…
ColorizationDeep LearningImage ColorizationAnalysis of Different Losses for Deep Learning Image Colorization
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically colorize an image, one can define well-sui…
ColorizationDeep LearningImage ColorizationBias in Automated Image Colorization: Metrics and Error Types
We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the origin…
ColorizationImage ColorizationPatch-Based Stochastic Attention for Image Editing
Attention mechanisms have become of crucial importance in deep learning in recent years. These non-local operations, which are similar to traditional patch-based methods in image processing, complement local convolutions…
ColorizationImage ColorizationImage InpaintingImage Super-Resolution+1Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks
Deep image steganography is a data hiding technology that conceal data in digital images via deep neural networks. However, existing deep image steganography methods only consider the visual similarity of container image…
ColorizationImage ColorizationImage SteganographySteganalysisSCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device. Ho…
ColorizationImage ColorizationSuper-ResolutionAstronomical Image Colorization and upscaling with Generative Adversarial Networks
Automatic colorization of images without human intervention has been a subject of interest in the machine learning community for a brief period of time. Assigning color to an image is a highly ill-posed problem because o…
ColorizationImage ColorizationImage Super-ResolutionSuper-ResolutionTemporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
Video colorization is a challenging and highly ill-posed problem. Although recent years have witnessed remarkable progress in single image colorization, there is relatively less research effort on video colorization and …
ColorizationImage Colorization