Deep Colorization (1605.00075v1)

Abstract: This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labelled color scribbles on the grayscale target image or a careful selection of colorful reference images (e.g., capturing the same scene in the grayscale target image). Unlike the previous methods, this paper aims at a high-quality fully-automatic colorization method. With the assumption of a perfect patch matching technique, the use of an extremely large-scale reference database (that contains sufficient color images) is the most reliable solution to the colorization problem. However, patch matching noise will increase with respect to the size of the reference database in practice. Inspired by the recent success in deep learning techniques which provide amazing modeling of large-scale data, this paper re-formulates the colorization problem so that deep learning techniques can be directly employed. To ensure artifact-free quality, a joint bilateral filtering based post-processing step is proposed. We further develop an adaptive image clustering technique to incorporate the global image information. Numerous experiments demonstrate that our method outperforms the state-of-art algorithms both in terms of quality and speed.

• The paper introduces a deep learning approach that reformulates image colorization as a regression task, reducing reliance on manual input.
• It leverages multi-level feature extraction and adaptive clustering to tailor the model for different image scenes and improve colorization accuracy.
• Results show enhanced PSNR and rapid processing, setting a new benchmark for automatic, high-quality image colorization.

Deep Colorization: An Evaluation

The paper "Deep Colorization" by Zezhou Cheng, Qingxiong Yang, and Bin Sheng presents an innovative approach to addressing the challenging problem of image colorization, which involves converting a grayscale image into a colorful version. Traditional methods in this field often require substantial user input or rely on finding highly similar reference images, making fully-automatic, high-quality colorization a significant hurdle.

Methodology

The authors propose leveraging deep learning techniques to reformulate the colorization problem as a regression task. This approach utilizes deep neural networks (DNNs) trained on a large-scale reference image database. The model operates under the assumption that deep learning can effectively learn complex mappings from grayscale to color images by capturing both local and global features.

Key components of the methodology include:

• Feature Extraction: The paper highlights the importance of multi-level feature extraction, incorporating low-level (patch features), mid-level (DAISY features), and high-level (semantic features) descriptors. These features are used as input to the deep neural networks.
• Adaptive Image Clustering: An adaptive image clustering technique is introduced to categorize reference images into different clusters, each representing a distinct scene. This clustering reduces training ambiguities and enhances colorization accuracy by ensuring that neural networks are tailored to specific image categories.
• Post-Processing: To address potential noise and artifacts, especially in low-texture areas, the authors apply joint bilateral filtering as a post-processing step, using the input grayscale image as guidance.

Results

Numerous experiments demonstrate the superiority of the proposed method in terms of both quality and computational efficiency. Compared to state-of-the-art algorithms, the deep learning-based approach achieves high-quality colorizations while maintaining robustness across various image scenes. Notably, the method operates without the reliance on meticulously chosen reference images, a common limitation in prior works.

Quantitatively, the paper reports that the proposed method consistently outperforms existing techniques when evaluated against a ground truth dataset, offering notable improvements in Peak Signal-to-Noise Ratio (PSNR) values. From a computational perspective, the model also exhibits favorable performance, rapidly colorizing input images—a significant advantage over more computationally intensive methods.

Implications and Future Directions

The implications of this research highlight the potential for deep learning to automate and improve processes traditionally reliant on user intervention or limited datasets. By eliminating the need for manually selected reference images, the proposed method widens the accessibility and application of image colorization techniques.

Future developments could explore expanding the database to include even more diverse image categories, potentially enhancing the model's applicability to a broader range of grayscale images. Additionally, integrating more sophisticated semantic features or leveraging advancements in neural network architectures could further elevate the quality and efficiency of the colorization process.

In summary, "Deep Colorization" sets a new benchmark for automatic image colorization using deep learning. Though not without limitations—such as the reliance on pre-trained models and potential issues with synthetic images—the contributions of this paper represent a significant step forward in the field, offering a foundation for future research and application across various domains in computer vision.