Deep Exemplar-based Colorization (1807.06587v2)

Abstract: We propose the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image. Rather than using hand-crafted rules as in traditional exemplar-based methods, our end-to-end colorization network learns how to select, propagate, and predict colors from the large-scale data. The approach performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image. More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references. In order to further reduce manual effort in selecting the references, the system automatically recommends references with our proposed image retrieval algorithm, which considers both semantic and luminance information. The colorization can be performed fully automatically by simply picking the top reference suggestion. Our approach is validated through a user study and favorable quantitative comparisons to the-state-of-the-art methods. Furthermore, our approach can be naturally extended to video colorization. Our code and models will be freely available for public use.

• The paper presents a dual-branch CNN approach that enhances color propagation control and ensures perceptually natural outputs.
• It employs a similarity sub-network with VGG-19 and a colorization sub-network with distinct chrominance and perceptual loss functions.
• An automatic reference image retrieval algorithm optimizes color matching, yielding superior semantic accuracy and user satisfaction.

Deep Exemplar-based Colorization: A Comprehensive Analysis

The paper "Deep Exemplar-based Colorization" presents an innovative approach for image colorization that leverages deep learning techniques. This paper pioneers the use of a deep learning framework for exemplar-based colorization, which addresses both controllability and robustness, setting it apart from many existing algorithms in the field.

The proposed method utilizes a convolutional neural network (CNN) to perform exemplar-based local colorization seamlessly. Unlike conventional exemplar-based methods which rely on meticulously crafted rules, this framework learns to select, propagate, and predict colors effectively from large-scale datasets. The incorporation of reference images allows for adjustable colorization outputs, granting users a degree of control over the final results—a feature not adequately addressed by many learning-based colorization methods.

The system architecture comprises two primary sub-networks: a Similarity sub-network and a Colorization sub-network. The Similarity sub-network employs a VGG-19 neural network to pre-process the input grayscale and reference images to gauge semantic similarity, thereby circumventing traditional errors introduced by low-level feature metrics. This is crucial for ensuring that the chosen reference reflects meaningful correspondence with the target grayscale image.

The Colorization sub-network is designed around solving the task in an end-to-end manner, incorporating two branches: the Chrominance and Perceptual branches. The Chrominance branch emphasizes faithful color propagation from the reference, while the Perceptual branch ensures natural color predictions where no reliable reference is available. This is achieved through multi-task learning with separate loss functions tailored to their specific tasks—chrominance loss for achieving color consistency and perceptual loss for high-level feature matching.

One standout feature introduced is an automatic image retrieval algorithm that recommends suitable reference images, enhancing ease of use and allowing the approach to function as a fully automated colorization system. This recommendation uses a blend of semantic and luminance information, aligning closely with the goals of producing perceptually pleasing images.

Quantitative and qualitative evaluations further underscore the efficiency and adaptability of the proposed approach across different benchmarks. The authors report superior classification accuracy of the colorized images, demonstrating the method’s capability in retaining semantic integrity. Moreover, this work is validated through user studies that reveal its appeal over other state-of-the-art learning-based methods.

The implications of this work are significant both practically and theoretically. Practically, it allows users to colorize images and videos with minimal manual intervention while maintaining high quality and accuracy. Theoretically, it opens new pathways for integrating reference-based controls in deep learning frameworks, expanding the scope of neural networks in creative tasks such as colorization.

Looking forward, the research suggests potential enhancements, including the ability to handle legacy images and video frames. The limitations described, such as handling unusual artistic references or dealing with significant luminance variations, suggest areas for further exploration. Future iterations of this model might incorporate more sophisticated networks to address these challenges, pushing towards more generalized solutions in complex colorization tasks.

In conclusion, "Deep Exemplar-based Colorization" effectively bridges the gap between the need for user control in colorization and the robustness offered by deep learning technologies, thereby significantly contributing to advancements in the field of vision-driven graphics.