6 accepted papers
Recorrupted-to-Recorrupted (R2R) has emerged as a methodology for training deep networks for image restoration in a self-supervised manner from noisy measurement data alone, demonstrating equivalence in expectation to the supervised squared loss in the case of Gaussian noise. However, its effectiven…
Commercial sensors often suffer from overexposure in bright regions, leading to signal clipping and information loss because of saturation. Existing solutions involve either employing logarithmic irradiance response sensors or capturing multiple shots from different saturation levels. However, these…
This paper introduces a plug-and-play algorithm for enhancing compressive spectral imaging (CSI) through the integration of both a quadratic envelope (QE) regularizer and a deep prior. Our method employs the QE-based regularizer to foster a low-rank structure in conjunction with deep priors, synergi…
Hadamard single-pixel imaging (HSI) is a promising sensing approach for acquiring spectral images in the near-infrared spectrum with high spatial resolution and fast recovery times due to the efficient invertible properties of the Hadamard matrix. The potential of the HSI system is diminished becaus…
Deep learning End-to-End (E2E) approaches have emerged as alternative optical design models, which jointly train the optical parameters of the sensing protocol, and the parameters of the deep neural network to achieve a specific task. This E2E model is particularly useful in the design of coding opt…
Phase retrieval is a kind of ill-posed inverse problem, which is present in various applications, such as optics, astronomical imaging, and X-ray crystallography. Mathematically this inverse problem consists on recovering an unknown signal x ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xm…