Abstract: Techniques related to generating holographic images for a holographic heads up display are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane of the heads up display.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images for a holographic heads up display are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane of the heads up display.

Abstract: Techniques related to generating holographic images for a holographic heads up display are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane of the heads up display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: A method and system of holographic image processing includes phase error compensation.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: In some examples, a three dimensional display system includes a display (for example, a display screen or a display panel), a micro lens array, and an eye tracker to track one or more eyes of a person and to provide eye location information. The display system also includes a rendering processor to render or capture color plus depth images (for example, RGB-D images) or light field images. The display system also includes a light field processor to use the eye location information to convert the rendered color plus depth images or light field images to display images to be provided to the display.

Abstract: In some examples, a three dimensional display system includes a display (for example, a display screen or a display panel), a micro lens array, and an eye tracker to track one or more eyes of a person and to provide eye location information. The display system also includes a rendering processor to render or capture color plus depth images (for example, RGB-D images) or light field images. The display system also includes a light field processor to use the eye location information to convert the rendered color plus depth images or light field images to display images to be provided to the display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.