Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision systems. However, additional causes of quality loss have been encountered in the development of such systems. This study investigates the influence on the reconstructed image quality of such factors as limitations of mass production technology for diffractive optics, lossy video stream compression artifacts, and specificities of a neural network approach to image reconstruction. The paper offers an end-to-end deep learning-based image reconstruction framework to compensate for the additional factors of quality losing. It provides the image reconstruction quality sufficient for applied vision systems.

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The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.

We present a collection of 24 multiple object scenes recorded under 18 multiple light source illumination scenarios each. The illuminants are varying in dominant spectral colours, intensity and distance from the scene. We mainly address the realistic scenarios for evaluation of computational colour constancy algorithms, but also have aimed to make the data as general as possible for computational colour science and computer vision. Along with the images, we provide also spectral characteristics of the camera, light sources, and the objects and include pixel-by-pixel ground truth annotation of uniformly coloured object surfaces. The dataset is freely available at https://github.com/visillect/mls-dataset.

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