DCA-LUT: Deep Chromatic Alignment with 5D LUT for Purple Fringing Removal

TL;DR

DCA-LUT is a novel deep learning framework that effectively removes purple fringing by learning an adaptive color space and applying a 5D LUT for precise color correction, outperforming existing methods.

Contribution

We introduce the first deep learning approach for purple fringing removal, featuring a novel chromatic-aware transformation and a 5D LUT for efficient color correction.

Findings

Achieves state-of-the-art results on synthetic and real datasets.

Introduces a large-scale synthetic purple fringing dataset (PF-Synth).

Demonstrates robust performance in diverse imaging conditions.

Abstract

Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introduce DCA-LUT, the first deep learning framework for purple fringing removal. Inspired by the physical root of the problem, the spatial misalignment of RGB color channels due to lens dispersion, we introduce a novel Chromatic-Aware Coordinate Transformation (CA-CT) module, learning an image-adaptive color space to decouple and isolate fringing into a dedicated dimension. This targeted separation allows the network to learn a precise ``purple fringe channel", which then guides the accurate restoration of the luminance channel. The final color correction is performed by a learned 5D Look-Up Table (5D LUT), enabling efficient and powerful% non-linear color mapping. To enable robust training and fair evaluation, we constructed a large-scale synthetic purple fringing dataset (PF-Synth). Extensive experiments in synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in purple fringing removal.