Task-Decoupled Bézier Surface Constraint for Uneven Low-Light Image Enhancement

Low-light image enhancement (LLIE) is a fundamental task in computer vision. Its goal is to extract more useful information from dark regions. Many existing methods have made excellent strides in improving image brightness and enhancing texture details. However, these approaches often lead to overexposure in certain regions when dealing with unevenly illuminated images, resulting in the loss of original information within the images. To address this issue, we propose a Bézier surface constraint (BSCNet) method based on task decoupling to enhance low-light images with uneven brightness. Specifically, we design a diffusion model with a branch structure that separates the enhancement process into brightness adjustment and color restoration, enabling independent control over brightness uniformity. Additionally, we use Bézier surfaces as a learning target to impose smoothness constraints on the image, thereby addressing the issue of uneven brightness in the enhanced image. To counteract potential detail loss introduced by Bézier surfaces, we introduce a spatial-frequency reconstruction module based on the Fourier transform to enhance fine-grained texture information. Experimental comparisons of six generalized LLIE datasets show that our proposed method has demonstrated outstanding effectiveness.