Live preview

Abstract: An image capturing apparatus includes: an imaging unit configured to capture an image of a subject and outputs image data containing the image; a display unit configured to display a live preview image of the subject based on the image data output from the imaging unit; a processor configured to set an image size of the image to be recorded and perform image processing on the live preview image to control a display range of the live preview image to be displayed on the display unit based on the image size to be recorded.

Abstract: A simplified image display control interface provides easy and convenient retrieving and viewing of digital images captured by a digital camera. Two control buttons provide for scrolling backwards or forwards through a plurality of captured images displayed on a display and stored in the digital camera memory. When the oldest captured image has been displayed, subsequent actuation of a control button causes the display to be turned off. When the most recently captured image is displayed, subsequent actuation of another control button causes a live preview to be displayed.

Abstract: Digital cameras are equipped with passive autofocus mechanisms where a lens is focused using only the camera's optical system and an algorithm for controlling the lens. The speed and accuracy of the autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed--reducing the number of iterations needed to focus by 37.9 % on average in common photography settings and 22.9 % on average in a more difficult focus stacking setting--while maintaining accuracy.

Abstract: We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable. Our approach is based on Markov Decision Process (MDP). In the camera viewfinder or live preview mode, given the current frame, our system predicts the change in exposure so as to optimize the trade-off among image quality, fast convergence, and minimal temporal oscillation. We model the exposure prediction function as a fully convolutional neural network that can be trained through Gaussian policy gradient in an end-to-end fashion. As a result, our system can associate scene semantics with exposure values; it can also be extended to personalize the exposure adjustments for a user and device. We improve the learning performance by incorporating an adaptive metering module that links semantics with exposure. This adaptive metering module generalizes the conventional spot or matrix metering techniques. We validate our system using the MIT FiveK [1] and our own datasets captured using iPhone 7 and Google Pixel. Experimental results show that our system exhibits stable real-time behavior while improving visual quality compared to what is achieved through native camera control.