基于雾浓度等级识别的笼养鸭饲料颗粒堆积图像增强方法

Image Enhancement Method for Images of Feed Pellet Accumulation in Cage-reared Ducks Based on Fog Density Level Recognition

摘要:

针对笼养高湿环境下饲料颗粒堆积量视觉检测中截面光条图像因水雾散射导致的退化问题,现有多曝光图像融合增强方法存在伽马变换校正针对性差、计算冗余和实时性不足等问题,提出一种基于雾浓度等级识别与双伽马校正的图像块频域融合增强方法。首先构建融合图像灰度统计特征与纹理特征的雾浓度等级识别模型,实现对雾浓度的等级识别;然后,采用同态滤波对图像预处理,根据识别的雾浓度等级,应用双伽马校正策略,生成亮度互补的图像对;接着,采用32像素×32像素滑动窗口(重叠率50%)将图像对分解为重叠子块,对各子块频域分解,低频分量通过t分布加权融合平衡亮度,高频分量基于细节显著性加权融合保持边缘细节,遵循水雾退化的物理特性分配二者的权重,再重构图像;最终经加窗累加与归一化,重建增强的完整图像。通过对6组不同雾浓度等级下2358幅图像的增强处理试验表明,相较于2种经典且广泛验证的图像增强方法,本方法取得较优综合性能:图像像素灰度之间的均方误差降低至0.0078,信噪比提升至10.18dB,结构相似度达0.89,图像熵为4.54,自然度指标NIQE为6.42,单帧图像平均处理时间小于0.06s。该图像增强方法有效抑制了水雾引起的对比度下降与细节模糊,为笼养环境下饲料颗粒堆积量的精准视觉检测奠定了基础,在畜禽笼养自动饲喂生产中具有广泛应用前景。

Abstract:

In response to the degradation of cross-sectional light stripe images caused by water fog scattering during the visual detection of feed pellet accumulation in high-humidity caged environments, a novel image block frequency-domain fusion enhancement method was proposed based on fog concentration level recognition and dual gamma correction. Existing multi-exposure image fusion enhancement methods exhibited limitations such as poor specificity, computational redundancy, and inadequate real-time performance. Firstly, a fog concentration level recognition model integrated image grayscale statistical features and texture features was constructed to achieve fog concentration level identification. Subsequently, homomorphic filtering was applied for image preprocessing, and a dual gamma correction strategy was implemented based on the identified fog concentration level to generate a pair of luminance- complementary images. Next, using a 32 pixel × 32 pixel sliding window with a 50% overlap rate, the image pair was decomposed into overlapping sub-blocks, followed by frequency domain decomposition of each sub-block. The low-frequency components were subjected to t-distribution weighted fusion to balance brightness, while the high-frequency components were processed by using detail saliency weighted fusion to preserve edge details. The weight distribution of both components adhered to the physical properties of mist-induced degradation, subsequently reconstructing the image. Finally, the fully enhanced image was reconstructed through windowed accumulation and normalization. Experiments conducted on 2 358 images across six different fog concentration levels demonstrated that, compared with two classical and extensively validated image enhancement methods, this approach achieved superior comprehensive performance: the mean square error between image pixel values was reduced to 0. 007 8, the signal-to-noise ratio was increased to 10. 18 dB, structural similarity reached 0. 89, image entropy was 4. 54, naturalness index NIQE was 6. 42, and the average processing time was less than 0. 06 s per frame. This image enhancement method effectively mitigated contrast degradation and detail blurring caused by water fog, thus providing a solid foundation for the accurate visual detection of feed pellet accumulation in caged environments. It demonstrated significant potential for widespread applications in automated feeding systems within poultry and livestock caged farming operations.