Image debanding using cross-scale invertible networks with banded deformable convolutions.

Abstract

Banding artifacts in images stem from limitations in color bit depth, image compression, or over-editing, significantly degrades image quality, especially in regions with smooth gradients. Image debanding is about eliminating these artifacts while preserving the authenticity of image details. This paper introduces a novel approach to image debanding using a cross-scale invertible neural network (INN). The proposed INN is information-lossless and enhanced by a more effective cross-scale scheme. Additionally, we present a technique called banded deformable convolution, which fully leverages the anisotropic properties of banding artifacts. This technique is more compact, efficient, and exhibits better generalization compared to existing deformable convolution methods. Our proposed INN exhibits superior performance in both quantitative metrics and visual quality, as evidenced by the results of the experiments.