Foggy weather reduces the visibility of photographed objects, causing image distortion and decreasing overall image quality. Many approaches (e.g., image restoration, image enhancement, and fusion-based methods) have been proposed to work out the problem. However, most of these defogging algorithms are facing challenges such as algorithm complexity or real-time processing requirements. To simplify the defogging process, we propose a fusional defogging algorithm on the linear transmission of gray single-channel. This method combines gray single-channel linear transform with high-boost filtering according to different proportions. To enhance the visibility of the defogging image more effectively, we convert the RGB channel into a gray-scale single channel without decreasing the defogging results. After gray-scale fusion, the data in the gray-scale domain should be linearly transmitted. With the increasing real-time requirements for clear images, we also propose an efficient real-time FPGA defogging architecture. The architecture optimizes the data path of the guided filtering to speed up the defogging speed and save area and resources. Because the pixel reading order of mean and square value calculations are identical, the shift register in the box filter after the average and the computation of the square values is separated from the box filter and put on the input terminal for sharing, saving the storage area. What’s more, using LUTs instead of the multiplier can decrease the time delays of the square value calculation module and increase efficiency. Experimental results show that the linear transmission can save 66.7% of the total time. The architecture we proposed can defog efficiently and accurately, meeting the real-time defogging requirements on 1920 × 1080 image size.

中文翻译：

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一种基于FPGA的实时有效融合图像去雾架构

有雾的天气会降低拍摄对象的可见度，导致图像失真并降低整体图像质量。已经提出了许多方法（例如，图像恢复、图像增强和基于融合的方法）来解决这个问题。然而，这些去雾算法中的大多数都面临着算法复杂性或实时处理要求等挑战。为了简化去雾过程，我们提出了一种灰度单通道线性传输的融合去雾算法。该方法将灰度单通道线性变换与不同比例的高升压滤波相结合。为了更有效地增强去雾图像的可见性，我们将 RGB 通道转换为灰度单通道而不降低去雾效果。灰度融合后，灰度域中的数据应该是线性传输的。随着对清晰图像的实时性要求越来越高，我们还提出了一种高效的实时FPGA去雾架构。该架构优化了引导滤波的数据路径，加快了去雾速度，节省了面积和资源。由于均值和平方值计算的像素读取顺序相同，将平均和平方值计算后的box filter中的移位寄存器与box filter分开放在输入端共享，节省存储空间. 更重要的是，使用LUT代替乘法器可以减少平方值计算模块的时间延迟，提高效率。实验结果表明，线性传输可以节省66.7%的总时间。