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Weighted Sparse Representation and Gradient Domain Guided Filter Pyramid Image Fusion Based on Low-Light-Level Dual-Channel Camera

机译:基于弱光双通道摄像机的加权稀疏表示与梯度域导引的金字塔图像融合

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摘要

Generally, the dynamic range of night vision scenes is large. Owing to the limited dynamic range of traditional low light imaging technology, the captured images are always partially overexposed or underexposed. Multi-exposure fusion is the most effective method of overcoming the dynamic range limitation of sensor, and multi-frame low dynamic range (LDR) image fusion is a key consideration. However, existing fusion methods have problems such as image detail blurring and image aliasing. This paper proposes an image multi-scale decomposition method based on gradient domain guided filter (GDGF), which can better extract image details. The fusion algorithm adopts different fusion strategies for different scales. The low-frequency layer of the image uses a new weighted sparse representation (wSR) method, which can eliminate the image boundary problems and more adequately retain the image edges.
机译:通常,夜视场景的动态范围很大。由于传统的弱光成像技术的动态范围有限,因此捕获的图像总是部分曝光过度或曝光不足。多重曝光融合是克服传感器动态范围限制的最有效方法,而多帧低动态范围(LDR)图像融合则是关键考虑因素。然而,现有的融合方法具有诸如图像细节模糊和图像混叠的问题。提出了一种基于梯度域导引滤波器(GDGF)的图像多尺度分解方法,可以更好地提取图像细节。融合算法针对不同规模采用不同的融合策略。图像的低频层使用新的加权稀疏表示(wSR)方法,该方法可以消除图像边界问题并更充分地保留图像边缘。

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