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.
