Abstract: Digital radiography imaging plays an important role in internal defect detection of aircraft engine casings. However, problems such as low contrast and blurred edges in original X-ray images seriously affect the detection efficiency and quality. To address these problems, an image enhancement method was proposed based on the fusion of local fuzzy entropy edge detection, adaptive gamma transform, and improved contrast-limited adaptive histogram equalization (CLAHE). Edge features were first extracted using local fuzzy entropy to construct a weight matrix, which was then fused with the original image to preserve and enhance defect contours. Adaptive gamma transform was applied to the fused image to dynamically adjust the overall image brightness and contrast, addressing the global brightness-darkness imbalance. Finally, improved CLAHE was used to enhance the contrast of local details and the overall signal-to-noise ratio. Experimental results demonstrate that, compared with state-of-the-art enhancement algorithms, the proposed method provides superior subjective visualization of fine defects inside the casing. In terms of objective metrics, the method achieves improvements of 2% in information entropy, 3% in peak signal-to-noise ratio, 4% in structural similarity index, and 5% in average gradient. The proposed technique offers reliable support for radiographic nondestructive testing of aircraft engine casings and presents significant potential for engineering applications.