Anti-Shake HDR Imaging Using RAW Image Data

Yan Liu,Wei Huang,Baohua Jin,Bingxue Lv,Canlin Li

doi:10.3390/info11040213

Abstract

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.

Highlights

Raw images capture all image data recorded by the sensor, providing many advantages compared with common compressed image formats, such as lossless compression, linear response to scene radiance, non-destructive white balance, wider color gamut, higher dynamic range, and so on
To better automatically correct the position offsets caused by camera shaking between multi-exposure image sequences, we introduced a reference image selection method based on information entropy for raw image sequences
(1): multi-exposure image sequences, we introduced a reference image selection method based on information entropy for raw image sequences

Summary

Introduction

Raw images capture all image data recorded by the sensor, providing many advantages compared with common compressed image formats, such as lossless compression, linear response to scene radiance, non-destructive white balance, wider color gamut, higher dynamic range (generally 12–14 bits), and so on. Most vision tasks are finished using 8-bit standard RGB (sRGB) compressed JPEGs because raw images require large amounts of memory space and are not well supported by many imaging applications. While compression algorithms may be optimized perfectly, the sRGB images are highly processed by the color Image Processing Pipeline (IPP) in terms of color and scene radiance. Researchers need to increase the complexity of algorithms to obtain better results when using the compressed JPEG formats in vision tasks. High-dynamic-range (HDR) imaging provides the capacity to capture, manipulate, and display real-world lighting in different imaging conditions, significantly improving the visual experience

Methods

Results

Conclusion