Apple inc. (20240107162). Extended depth of field using deep learning simplified abstract

Extended depth of field using deep learning

Organization Name

apple inc.

Inventor(s)

Dan C. Lelescu of Grandvaux (CH)

Rohit Rajiv Ranade of Pleasanton CA (US)

Noah Bedard of Los Gatos CA (US)

Brian Mccall of San Jose CA (US)

Kathrin Berkner Cieslicki of Los Altos CA (US)

Michael W. Tao of San Jose CA (US)

Robert K. Molholm of Scotts Valley CA (US)

Toke Jansen of Holte (DK)

Vladimir Krneta of San Jose CA (US)

Extended depth of field using deep learning - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240107162 titled 'Extended depth of field using deep learning

Simplified Explanation

The method described in the abstract involves capturing multiple input images of a scene with different focal depths and fields of view, aligning and preprocessing these images, and processing them in a neural network to generate an output image with an extended depth of field.

• Capturing multiple input images of a scene with varying focal depths and fields of view
• Preprocessing and aligning the input images
• Processing the aligned images in a neural network
• Generating an output image with an extended depth of field

Potential Applications

This technology could be applied in fields such as photography, medical imaging, and surveillance for enhancing image quality and focus.

Problems Solved

This technology addresses the issue of limited depth of field in traditional imaging systems, allowing for clearer and more detailed images across different focal depths.

Benefits

The method improves image quality by extending the depth of field, resulting in sharper and more focused images. It also enhances the overall visual experience for viewers.

Potential Commercial Applications

This technology could be utilized in camera systems, medical imaging devices, and security cameras to enhance image clarity and focus, potentially leading to improved diagnostic capabilities and surveillance effectiveness.

Possible Prior Art

One possible prior art could be the use of image stacking techniques to enhance depth of field in photography, although the specific method described in the abstract may offer unique advantages in terms of alignment and processing.

Unanswered Questions

How does the neural network determine the optimal depth of field for the output image?

The abstract does not provide details on how the neural network calculates the extended depth of field in the output image.

What is the computational complexity of the image alignment and processing in the neural network?

The abstract does not mention the computational resources required for aligning and processing the input images, which could be a crucial factor in practical applications.

Original Abstract Submitted

a method for image enhancement includes capturing multiple input images of a scene, including at least a first input image having a first field of view (fov) captured with a first focal depth and a second input image having a second fov captured with a second focal depth. the input images in the sequence are preprocessed so as to align the images. the aligned images are processed in a neural network, which generates an output image having an extended depth of field encompassing at least the first and second focal depths.