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A generalized logarithmic image processing model based on the gigavision sensor model.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 8, 2011

Summary

This summary is machine-generated.

Researchers developed a generalized logarithmic image processing (GLIP) model, offering new image operations. This GLIP model, an extension of the LIP model, proves effective for image processing tasks like tone mapping in image dehazing.

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Area of Science:

Image Processing
Mathematical Modeling
Computer Vision

Background:

The logarithmic image processing (LIP) model offers generalized linear operations for image processing.
The gigavision sensor (GVS) is a novel imaging device with a statistical model.
Existing image processing techniques often require specialized models for different imaging devices.

Discussion:

This paper introduces a generalized logarithmic image processing (GLIP) model by integrating concepts from the LIP model and statistical models of new imaging devices like the gigavision sensor (GVS).
The GLIP model extends the LIP model, providing novel image representations and operations applicable to a broader range of image processing problems, not limited to GVS data.
A new parametric LIP model is also presented, enhancing the theoretical framework.

Key Insights:

The developed GLIP model serves as a generalized framework, encompassing the original LIP model as a special case.
New scalar multiplication operations are defined within the GLIP model, offering advanced image manipulation capabilities.
An energy-preserving algorithm for tone mapping, crucial for image dehazing, is proposed, demonstrating the practical utility of the new scalar multiplication operation.

Outlook:

The GLIP model and its associated operations offer a versatile platform for advancing various image processing applications.
Further research can explore the application of the GLIP model in other areas of computer vision and image analysis.
The proposed tone mapping algorithm shows promise for improving the quality of dehazed images.