Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by illumination and improve visual quality of such images. However, these loss defect-discriminative information a high computational burden. To address above issues, we propose joint-prior-based (JPUIE) method. Specifically, semi-coupled retinex model is first constructed to accurately Furthermore, multiscale Gaussian-difference-based background prior proposed reweight data consistency term, thereby avoiding enhanced image. Last, using powerful nonlinear fitting ability deep neural networks, denoised replace existing physics priors, reducing time consumption. Various experiments are carried out on public private datasets, which used compare images results symmetric way. The experimental demonstrate that our method more conducive downstream inspection tasks than other methods.