Abstract:
In low-visibility scenes, image enhancement is a crucial pre-processing step for many computer vision applications. We develop a unified two-pathway model inspired by biological vision, especially early visual mechanisms, to enhance images using low dynamic range (LDR) and high dynamic range (HDR) tone mapping.
First, the input image is split into two visual pathways: structure-pathway and detail-pathway, which correspond to the M- and P-pathways in the early visual system, which code low- and high-frequency visual information, respectively.
An extended biological normalization model integrates global and local luminance adaptation in the structure-pathway to handle visual scenes with different illuminations. Local energy weighting-based detail-pathways enhance detail and suppress local noise.
Integrating structure-and-detail-pathway outputs enhances low-light images. With some fine-tuning, the model can also tone map HDR images. The proposed model outperforms state-of-the-art methods in visual enhancement tasks on two LDR image datasets and one HDR scene dataset.
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