Intrinsic Image Transfer for Illumination Manipulation

TL;DR

This paper introduces a new intrinsic image transfer algorithm for local illumination manipulation that offers a closed-form solution, surpassing traditional Retinex-based methods, and demonstrates versatility in various illumination tasks.

Contribution

The paper proposes a novel intrinsic image transfer framework that directly operates on images without requiring intrinsic decomposition, enabling efficient and high-quality illumination manipulation.

Findings

Effective illumination compensation demonstrated.

High-quality results on natural image datasets.

Versatility across multiple illumination-related tasks.

Abstract

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets.