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Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial

Bhawna Goyal¹, Ayush Dogra², Ammar Jalamneh³

¹Department of UCRD and ECE, Chandigarh University, Mohali, Punjab, 140413, India. bhawnagoyal28@gmail.com.

Scientific Reports

|July 25, 2024

Summary

This summary is machine-generated.

This study introduces a new framework for low-light image enhancement using dictionary learning and a camera response model. It improves detail presentation and color integrity, offering better performance for degraded images.

Keywords:

Camera response function (CRF)Dictionary learning Image enhancement Innovative Research Technology

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Area of Science:

Computer Vision
Image Processing

Background:

Low-light conditions degrade image quality, impacting device performance.
Balancing light intensity, detail, and color is challenging in low-light enhancement.

Purpose of the Study:

To present a novel image enhancement framework for low-light conditions.
To improve detail presentation and color integrity in degraded images.

Main Methods:

Utilizes dictionary learning for sparse characterization of image detail patches.
Incorporates an edge-aware filter for detail enhancement.
Employs a camera response model (CRM) with illumination estimation for pixel exposure adjustment.

Main Results:

The proposed method generates high-quality enhanced images with improved detail visibility.
Experimental analysis confirms the method's advantage in producing enhanced results with acceptable distortions.

Conclusions:

The novel framework effectively enhances low-light images by balancing key properties.
The approach is generalizable to various applications like remote sensing, medical imaging, and adverse conditions.