To address issues such as low brightness, low contrast, and lack of details in images taken under challenging conditions like nighttime, backlighting, and severe weather, a nonlinear adaptive dark detail enhancement algorithm is proposed for improving low-light images. To ensure color authenticity, the original image is first converted to HSV space, and the brightness component V is extracted. For dealing with the issues of poor brightness and low contrast in low-light images, an improved gamma correction algorithm is then adopted to adaptively adjust image brightness. Subsequently, a brightness adaptive contrast enhancement algorithm is introduced, combining a low-pass filtering approach to adaptively enhance high-frequency details. This helps highlight textures and edge information of dark areas of the image. Finally, a brightness-guided adaptive image fusion algorithm is proposed to preserve edge details in highlighted areas while avoiding overexposure. Experimental results demonstrate that the proposed algorithm effectively adapts to the image characteristics of low-light environments. It not only significantly enhances the brightness and contrast of low-light images but also highlights details in darker areas while preserving color authenticity.