Bit-depth color recovery via off-the-shelf super-resolution models

TL;DR

This paper presents a novel method that uses super-resolution architectures to recover high bit-depth color information from images, outperforming existing techniques by leveraging detailed spatial features.

Contribution

The paper introduces a super-resolution-based approach for bit-depth color recovery, utilizing interpolated data and spatial features to enhance color detail restoration.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Leverages super-resolution to extract detailed color information

Achieves pixel-level recovery of fine-grained color details

Abstract

Advancements in imaging technology have enabled hardware to support 10 to 16 bits per channel, facilitating precise manipulation in applications like image editing and video processing. While deep neural networks promise to recover high bit-depth representations, existing methods often rely on scale-invariant image information, limiting performance in certain scenarios. In this paper, we introduce a novel approach that integrates a super-resolution architecture to extract detailed a priori information from images. By leveraging interpolated data generated during the super-resolution process, our method achieves pixel-level recovery of fine-grained color details. Additionally, we demonstrate that spatial features learned through the super-resolution process significantly contribute to the recovery of detailed color depth information. Experiments on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, highlighting the potential of super-resolution for high-fidelity color restoration.