LapLoss: Laplacian Pyramid-based Multiscale loss for Image Translation
Journal:
arXiv
Published Date:
Mar 7, 2025
Abstract
Contrast enhancement, a key aspect of image-to-image translation (I2IT),
improves visual quality by adjusting intensity differences between pixels.
However, many existing methods struggle to preserve fine-grained details, often
leading to the loss of low-level features. This paper introduces LapLoss, a
novel approach designed for I2IT contrast enhancement, based on the Laplacian
pyramid-centric networks, forming the core of our proposed methodology. The
proposed approach employs a multiple discriminator architecture, each operating
at a different resolution to capture high-level features, in addition to
maintaining low-level details and textures under mixed lighting conditions. The
proposed methodology computes the loss at multiple scales, balancing
reconstruction accuracy and perceptual quality to enhance overall image
generation. The distinct blend of the loss calculation at each level of the
pyramid, combined with the architecture of the Laplacian pyramid enables
LapLoss to exceed contemporary contrast enhancement techniques. This framework
achieves state-of-the-art results, consistently performing well across
different lighting conditions in the SICE dataset.
