Learning the image processing pipeline

Name: Learning the image processing pipeline
License: https://opendatacommons.org/licenses/by/1-0/

<a href="https://embed.stanford.edu/iframe/?url=https%3A%2F%2Fpurl.stanford.edu%2Fbk962py0458" class="su-underline">Show Content</a>

Abstract/Contents

Abstract: Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image-processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image-processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.

Type of resource	software, multimedia
Date created	May 2016

Subject	camera image processing pipeline
Subject	digital camera
Subject	demosaicking
Subject	denoising
Subject	machine learning
Genre	Dataset

Related Publication	Lansel, Steven, and Brian Wandell. "Local linear learned image processing pipeline." Imaging Systems and Applications 10 Jul. 2011: IMC3.
Related Publication	Tian, Qiyuan et al. "Automating the design of image processing pipelines for novel color filter arrays: Local, Linear, Learned (L3) method." IS&T/SPIE Electronic Imaging 7 Mar. 2014: 90230K-90230K-8.
Related Publication	Jiang, Haomiao et al. "Local Linear Approximation for Camera Image Processing Pipelines.", Electronic Imaging, 2016
Related Publication	Tian, Qiyuan, and Haomiao Jiang. "Accelerating a learning–based image processing pipeline for digital cameras." (2015).
Related item	Title The L3 repository Title Link to Image Systems Engineering Toolbox
Location	https://purl.stanford.edu/bk962py0458

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
License: This work is licensed under an Open Data Commons Attribution License v1.0.

Preferred Citation: Jiang, Haomiao and Tian, Qiyuan and Farrell, Joyce and Wandell, Brian. (2016). Learning the image processing pipeline. Stanford Digital Repository. Available at: http://purl.stanford.edu/bk962py0458

VISTA Lab

Loading usage metrics...