Exposure

Abstract: A solid-state image sensor which detects the quantity of light incident on image sensor cells during exposure and performs exposure control in accordance with an exposure value thus obtained The solid-state image sensor is provided with a photoelectric converter having a plurality of non-destructive readout type image sensor cells arranged in a matrix form; an exposure detector for reading out stored information of predetermined ones of the image sensor cells during exposure, obtaining an added value of the stored information thus read out and detecting that the added value reaches a predetermined level; an exposure controller which is supplied with the detected signal from the exposure detector to control at least one of the charge storage time of each image sensor cell by an optical signal, the intensity of light incident on the image sensor cell and the photosensitivity of the image sensor cell; and a scanner for scanning the photoelectric converter to read out stored information of the image sensor cells during exposure

Abstract: Imaging performance metrics of single-bit and multi-bit photo-electron-counting quanta image sensors (QIS) are analyzed using Poisson arrival statistics. Signal and noise as a function of exposure are determined. The D-log H characteristic of single-bit sensors including overexposure latitude is quantified. Linearity and dynamic range are also investigated. Read-noise-induced bit-error rate is analyzed and a read-noise target of less than 0.15 e-rms is suggested.

Abstract: Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.

Abstract: Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting in dark regions. Both under- and overexposure greatly reduce the contrast and visual appeal of an image. Prior work mainly focuses on underexposed images or general image enhancement. In contrast, our proposed method targets both over- and underexposure errors in photographs. We formulate the exposure correction problem as two main sub-problems: (i) color enhancement and (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deep neural network (DNN) model, trainable in an end-to-end manner, that addresses each sub-problem separately. A key aspect of our solution is a new dataset of over 24,000 images exhibiting the broadest range of exposure values to date with a corresponding properly exposed image. Our method achieves results on par with existing state-of-the-art methods on underexposed images and yields significant improvements for images suffering from overexposure errors.