Camouflaged Object Detection (COD) is a crucial and challenging task for computer vision, which hinges on effectively distinguishing camouflaged objects from complex backgrounds. These objects often show diverse scales, blurred appearances, and a high degree of similarity to their surroundings. To address this issue, we proposed a kind of image enhancement methods for camouflaged object images to effectively enhance the visual features. Specifically, we proposed an Adaptive Canny (AdaCanny) algorithm which leveraged local brightness perception to adaptively enhance the images. The enhancement was achieved by integrating the texture features extracted via Circular Local Binary Pattern (CLBP) with the edge features obtained through AdaCanny. Consequently, the feature representation capability of camouflaged object images was significantly improved. We used our method in four camouflaged object image datasets and evaluated the performance with five COD models. The results demonstrated that the performance of the enhanced datasets was improved by 2% comparing with the original datasets. Our code is available at https://github.com/bachelorcy/Enhancement-of-CAM .