High Dynamic Range (HDR) imaging with modulo cam-eras involves solving a challenging inverse problem, where degradation occurs due to the modulo operation applied to the target HDR image.

Zhao et al. [58] used a modulo camera that is able to wrap the high radiance of dynamic range scene pe-riodically and save modulo information, then used Markov Random Field to unwrap and predict …

Positional learning trains the model to learn the modulo-2 position of pixels, leveraging the translation-invariance of CNN to replicate the underlying mosaic and its potential inconsistencies.

We evaluate our DUN, PnP-UA and UnModNet which are specifically designed for modulo HDR reconstruction, on the sample of real-sensor dataset [2]. This dataset collects 8-bit grayscale modulo …

Without additional illumination modules, passive 3D imaging systems exploit unconven-tional sensors for HDR imaging, such as single-photon avalanche diodes [17], quanta image sensors [11, 13], event …

To this aim we designed a CNN structure inspired from demosaicing algorithms and directed at classifying image blocks by their position in the image modulo (2 x 2).

This paper describes an approach to visual question an-swering based on a new model architecture that we call a neural module network (NMN). This architecture makes it possible to answer natural …

Positional learning trains the model to learn the modulo-2 position of pixels, leveraging the translation-invariance of CNN to replicate the underlying mosaic and its potential inconsistencies.

To this aim we designed a CNN structure inspired from demosaicing algorithms and directed at classifying image blocks by their position in the image modulo (2 × 2).

Consequently, the original phase image, which exhibits strong smoothness, is degraded to a wrapped version with both incorrect values and artificial discontinuities induced by the modulo opera-tion, …