Deep Quantigraphic Image Enhancement via Comparametric Equations

Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this constraint while retaining high efficiency, we propose a novel trainable module that diversifies the conversion from the low-light image and illumination map to the enhanced image. It formulates image enhancement as a comparametric equation parameterized by a camera response function and an exposure compensation ratio. By incorporating this module in an illumination estimation-centric DNN, our method improves the flexibility of deep image enhancement, limits the computational burden to illumination estimation, and allows for fully unsupervised learning adaptable to the diverse demands of different tasks.Comment: Published in ICASSP 2023. For GitHub code, see https://github.com/nttcslab/con

Consistent joint photometric and geometric image registration

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms

Realization of High Dynamic Range Imaging in the GLORIA Network and Its Effect on Astronomical Measurement

Citizen science project GLORIA (GLObal Robotic-telescopes Intelligent Array) is a first free- and open-access network of robotic telescopes in the world. It provides a web-based environment where users can do research in astronomy by observing with robotic telescopes and/or by analyzing data that other users have acquired with GLORIA or from other free-access databases. Network of 17 telescopes allows users to control selected telescopes in real time or schedule any more demanding observation. This paper deals with new opportunity that GLORIA project provides to teachers and students of various levels of education. At the moment, there are prepared educational materials related to events like Sun eclipse (measuring local atmosphere changes), Aurora Borealis (calculation of Northern Lights height), or transit of Venus (measurement of the Earth-Sun distance). Student should be able to learn principles of CCD imaging, spectral analysis, basic calibration like dark frames subtraction, or advanced methods of noise suppression. Every user of the network can design his own experiment. We propose advanced experiment aimed at obtaining astronomical image data with high dynamic range. We also introduce methods of objective image quality evaluation in order to discover how HDR methods are affecting astronomical measurements

IMAGE QUALITY IMPROVEMENT BY ADAPTIVE EXPOSURE CORRECTION TECHNIQUES

The proposed paper concerns the processing of images in digital format and, more specifically, particular techniques that can be advantageously used in digital still cameras for improving the quality of images acquired with a non-optimal exposure. The proposed approach analyses the CCD/CMOS sensor Bayer data or the corresponding color generated image and, after identifying specific features, it adjusts the exposure level according to a ‘camera response ’ like function. 1

Prototype of high dynamic range camera

Запророновано реалізацію камери з широким динамічним діапазоном на ПЛІС з використанням сенсору NSC1005 для спеціальних потреб. Представлені базові метамитичні співвідношення, які описують метод розширення динамічного діапазону камери.The implementation of a camera with a high dynamic range on the FPGA using the NSC1005 sensor for special needs is proposed. Basic mathematical relationships are presented which describes a method for expanding the dynamic range of a camera.Предложено реализацию камеры с широким динамическим диапазоном на ПЛИС с использованием сенсора NSC1005 для специальных нужд. Представлены базовые математические соотношения, описывающие метод расширения динамического диапазона камеры

Practical photoquantity measurement using a camera

International audienceAn image output by a camera is generally not a faithful representation of the real scene, because it undergoes a series of radiometric disturbances during the imaging process. This paper proposes a method for obtaining a more accurate measure of the light seen by a camera. Our method requires no specific calibration apparatus and only minimal supervision. Nevertheless, it is quite comprehensive, since it accounts for response function, exposure, vignetting, spatial non-uniformity of the sensor and colour balancing. Our method works in two steps. First, the camera is calibrated off-line, in a photoquantity sense. Then, the photoquantity of any scene can be estimated in-line. Our method is therefore geared to a wide range of computer vision applications where a camera is expected to give a measurement of the visible light. The paper starts by presenting a photoquantity model of the camera imaging process. It then describes the key steps of calibration and correction method. Finally, results are given and analyzed to evaluate the relevance of our approach

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image

An Asynchronous Kalman Filter for Hybrid Event Cameras

Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement).Comment: 12 pages, 6 figures, published in International Conference on Computer Vision (ICCV) 202