Aspects of the present disclosure relate aids for the visually impaired.

The geometric pose of a patch of watermark data is estimated based on the position of a similar, but non-identical, patch of information within a data structure. The information in the data structure corresponds to a tiled array of calibration patterns that is sampled along at least three non-parallel paths. In a particular embodiment, the calibration patterns are sampled so that edges are globally-curved, yet locally-flat. Use of such information in the data structure enables enhanced pose estimation, e.g., speeding up operation, enabling pose estimation from smaller patches of watermark signals, and/or enabling pose estimation from weaker watermark signals. A great variety of other features and arrangements are also detailed.

Embodiments relate to circuitry for performing fusion of two images captured with two different exposure times to generate a fused image having a higher dynamic range. Information about first keypoints is extracted from the first image by processing pixel values of pixels in the first image. A model describing correspondence between the first image and the second image is then built by processing at least the information about first keypoints. A processed version of the first image is warped using mapping information in the model to generate a warped version of the first image spatially more aligned to the second image than to the first image. The warped version of the first image is fused with a processed version of the second image to generate the fused image.

An electric heater fire extinguishing strategy customization mechanism includes: a timing device for outputting the current operation time; a real-time display device installed onto an integrated circuit board of the electric heater and connected to the timing device for receiving the current operation time and displaying a red string corresponding to the real-time display and too-long operation information of when the current operation time exceeds a limit; a position sensing device installed onto the dot-matrix imaging mechanism for detecting the current position of the dot-matrix imaging mechanism, determining the difference between the current position and a predetermined fixed position, wherein the difference includes a horizontal change and a vertical change, and issuing a jitter sensing signal when the horizontal change changes between positive and negative values or the vertical change changes between positive and negative values. The electric heater includes a fire extinguishing function.

A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image integration performs a deep image integration learning on multi-modality training images and corresponding desired integrated images to generate a deep image integration model, which is applied to transform multi-modality images into a high quality integrated image mimicking a desired integrated image.

A computerized robust deep image transformation method performs a deep image transformation learning on multi-variation training images and corresponding desired outcome images to generate a deep image transformation model, which is applied to transform an input image to an image of higher quality mimicking a desired outcome image. A computerized robust training method for deep image prediction performs a deep image prediction learning on universal modality training images and corresponding desired modality prediction images to generate a deep image prediction model, which is applied to transform universal modality images into a high quality image mimicking a desired modality prediction image.

A method of video deblurring by an electronic device is described. The processing circuitry of the electronic device acquires N continuous image frames from a video clip the N being a positive integer, and the N continuous image frames including a blurry image frame to be processed. The processing circuitry of the electronic device performs three-dimensional (3D) convolution processing on the N continuous image frames with a generative adversarial network model, to acquire spatio-temporal information corresponding to the blurry image frame. The spatio-temporal information includes spatial feature information of the blurry image frame, and temporal feature information between the blurry image frame and a neighboring image frame of the N continuous image frames The processing circuitry of the electronic device performs deblurring processing on the blurry image frame by using the spatio-temporal information corresponding to the blurry image frame through the generative adversarial network model, to output a sharp image frame.

A method for correcting blur effects is presented. The method includes generating a plurality of images from a camera, synthesizing blurred images from sharp image counterparts to generate training data to train a structure-and-motion-aware convolutional neural network (CNN), and predicting a camera motion and a depth map from a single blurred image by employing the structure-and-motion-aware CNN to remove blurring from the single blurred image.

An image processing apparatus includes an information acquisition unit configured to acquire first optical information outside an image circle of an imaging optical system, and a processing unit configured to perform sharpening processing, based on the first optical information, for an input image generated by imaging using the imaging optical system. The first optical information is optical information generated based on second optical information within the image circle of the imaging optical system.

There is provided an image processing device including a light sensor and a processor. The light sensor is used to detect light and output an image frame. The processor identifies intensity of ambient light according to an image parameter associated with the image frame. When the ambient light is identified to be strong enough, the processor performs an object identification directly using the image frame. When the ambient light is identified to be not enough, the processor firstly converts the image frame to a converted image using a machine learning model, and then performs the object identification using the converted image.