Retinex Theory Enables Homogeneous Decomposition for Concealed Object Segmentation
A recent preprint on arXiv (2605.15450) presents RIDE, a novel technique for Concealed Object Segmentation (COS) that utilizes Retinex theory to separate images into their illumination and reflectance elements within a unified spatial domain. In contrast to current methods that either process RGB images or employ varied decompositions (such as Fourier or wavelet), RIDE maintains a consistent decomposition that ensures direct pixel-aligned cues. The primary revelation is that visual entanglement promotes appearance matching in the composite space, although it may not apply in both component spaces. COS encompasses various applications, including detecting camouflaged objects, segmenting polyps, identifying transparent objects, and inspecting industrial defects.
Key facts
- arXiv preprint 2605.15450 introduces RIDE
- RIDE uses Retinex theory for homogeneous image decomposition
- Decomposition separates illumination and reflectance components in the same spatial domain
- Existing methods use heterogeneous decompositions like Fourier or wavelet
- Visual entanglement does not require simultaneous matching in both component spaces
- COS includes camouflaged object detection, polyp segmentation, transparent object detection, and industrial defect inspection
- Homogeneous decomposition preserves pixel-aligned cues
- The method offers a fundamentally different perspective from prior work
Entities
Institutions
- arXiv