Perception-based Histogram Equalization for tone mapping applications

Ploumis, Stelios; Boitard, Ronan; Pourazad, Mahsa T.; Nasiopoulos, Panos

doi:10.1109/dmiaf.2016.7574892

Cited by 6 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the sense that our our approach plays with the effective dynamic range and the contrast of the image, our work is related to tone mapping, a process of mapping real world colors to a restricted color space for displaying image while preserving details. Traditional tone mapping algorithms employ variants of histogram equalization [84,15,18,12,64] to enhance the contrast of LDR images. Many recent works [56,21,20,54] leverage deep learning to recover the missing details in the over-exposed image regions by expanding the dynamic range of single LDR images.…”

Section: Related Workmentioning

confidence: 99%

Stay Positive: Non-Negative Image Synthesis for Augmented Reality

Luo

Yang

Xian

et al. 2022

Preprint

View full text Add to dashboard Cite

In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image. Most image generation methods, however, are ill-suited to this problem setting, as they make the assumption that one can assign arbitrary color to each pixel. In fact, naive application of existing methods fails even in simple domains such as MNIST digits, since one cannot create darker pixels by adding light. We know, however, that the human visual system can be fooled by optical illusions involving certain spatial configurations of brightness and contrast. Our key insight is that one can leverage this behavior to produce high quality images with negligible artifacts. For example, we can create the illusion of darker patches by brightening surrounding pixels. We propose a novel optimization procedure to produce images that satisfy both semantic and non-negativity constraints. Our approach can incorporate existing state-of-the-art methods, and exhibits strong performance in a variety of tasks including image-to-image translation and style transfer.

show abstract