Enhancing Near-Infrared to RGB Spectrum Translation with Multi-Scale HSV Color Feature Embedding

Core Concepts

A multi-scale HSV color feature embedding learning paradigm (MCFNet) is proposed to effectively solve the mapping ambiguity between near-infrared (NIR) and RGB domains, and balance the fidelity and diversity of texture details and color variations in NIR to RGB spectrum translation.

Abstract

The proposed MCFNet decomposes the challenging NIR-to-RGB spectral domain translation task into three sub-tasks: NIR texture maintenance, coarse geometry reconstruction, and RGB color prediction.
For the NIR texture maintenance, the Texture Preserving Block (TPB) extracts the near-infrared Laplacian component and injects it into the colorized output to improve texture fidelity.
The HSV Color Feature Embedding Module (HSV-CFEM) converts NIR inputs into HSV color space to effectively describe and distinguish different colors, and also serves as color guidance for the Geometry Reconstruction Module (GRM).
The GRM learns the contextual information from NIR inputs at a coarse level. The multi-scale color feature maps generated by HSV-CFEM are adaptively injected into the corresponding scales of GRM through the SPADE module to provide color guidance for geometry feature reconstruction.
Finally, the information obtained from all branches is fused using SPADE to generate the final high-fidelity colorized NIR image.
The proposed MCFNet demonstrates substantial performance gains over existing NIR image colorization methods in terms of PSNR, SSIM, AE, and LPIPS metrics. The qualitative results also show that MCFNet can generate colorized NIR images with vivid colors and well-preserved texture details.

Stats

Similar pixel values in the NIR domain can have very different RGB values, and vice versa, causing conventional image-to-image translation methods to produce monotonous or erroneous predictions.
The proposed MCFNet achieves a PSNR of 20.34, SSIM of 0.61, AE of 3.79, and LPIPS of 0.208, outperforming other state-of-the-art NIR colorization methods.

Quotes

"The NIR-to-RGB spectral domain translation is a formidable task due to the inherent spectral mapping ambiguities within NIR inputs and RGB outputs."
"We find that the mapping between NIR and RGB domains is highly unpredictable, e.g., similar pixel values in the NIR domain can have very different RGB values and vice versa."

Key Insights Distilled From

Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation

by Huiyu Zhai,M... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16685.pdf

Deeper Inquiries

How can the proposed MCFNet be extended to handle other types of image-to-image translation tasks beyond NIR-to-RGB colorization

The Multi-scale HSV Color Feature Embedding Network (MCFNet) can be extended to handle other image-to-image translation tasks beyond NIR-to-RGB colorization by adapting its modular design and learning paradigm to suit the specific characteristics of different domains. For instance, in tasks like grayscale image colorization or style transfer, the network can be modified to focus more on texture preservation and color feature embedding relevant to those domains. By adjusting the modules such as the Texture Preserving Block (TPB) and the HSV Color Feature Embedding Module (HSV-CFEM) to cater to the requirements of the new task, the MCFNet can effectively learn the mapping between different image domains.

What are the potential limitations of the HSV color feature embedding approach, and how could it be further improved to handle more challenging color mapping scenarios

The HSV color feature embedding approach, while effective in capturing and distinguishing color features, may have limitations in handling extremely complex or subtle color mapping scenarios where the differences between source and target domains are more nuanced. To improve its performance in such cases, the HSV-CFEM module could be enhanced by incorporating attention mechanisms to focus on specific color regions or by introducing adversarial training to refine the color feature embeddings. Additionally, integrating self-supervised learning techniques or leveraging larger and more diverse datasets can help the network learn a more robust representation of color features, enhancing its ability to handle challenging color mapping scenarios.

Given the importance of texture preservation in NIR-to-RGB translation, how could the Texture Preserving Block be enhanced to better capture and transfer high-frequency details across domains

To better capture and transfer high-frequency details across domains for texture preservation in NIR-to-RGB translation, the Texture Preserving Block (TPB) can be enhanced in several ways. One approach could involve incorporating advanced edge detection algorithms or texture analysis methods to extract more intricate texture features from the NIR input. Additionally, introducing feature normalization techniques or residual connections within the TPB can help maintain the fidelity of high-frequency details during the colorization process. Moreover, exploring the use of generative adversarial networks (GANs) or perceptual loss functions within the TPB can further improve the preservation of texture details in the translated images.