Abstarct: A Wireless Capsule Endoscopy is a device that examines the Gastrointestinal tract without causing injury or pain. According to the complex environment of the Gastrointestinal tract and the limited brightness of the capsules, the recorded images often have poor illumination. furthermore, the limited depth of field of the capsule camera reduces the visibility and loss of image detail. On the other hand, the image quality of Wireless Capsule Endoscopy is not completely clear and has noise due to the limitations of communication bandwidth and low transmission power of the recorded images. Also, the noise intensity of Wireless Capsule Endoscopy images varies according to different Gastrointestinal environments, and the variety of cameras and reference images are not available. Since medical images play a very important role in the diagnosis of diseases of the internal organs of the body, so the quality and clarity of medical images directly affect the diagnosis of physicians. Therefore, our aim in this study is to improve the brightness and denoising of Wireless Capsule Endoscopy images by preserving the detail of the image texture and without the need for reference images. Wireless Capsule Endoscopy images have poor brightness and contrast. Therefore, to improve the images, it is necessary to improve the brightness and contrast of the images, while preserving the original color and locally. Therefore, the local gamma correction method has been used to improve the images. Then, according to the sensitivity of the human visual system to areas with a smooth texture, denoising is done locally and according to the texture of the images. Therefore, in the proposed method, first, according to the texture information, the image is divided into two smooth and non-smooth area, then by using a homomorphic filter, illumination, and reflectance components are extracted from the image, each of which has better contrast than the original image. Then, denoising is performed by applying an Anisotropic Diffusion filter on the output components of the homomorphic filter and selecting the appropriate recursive coefficient for areas with different textures. The proposed method, in addition to improving the images, preserving the details and edges of the images, also preserves the texture of the images without change. Quantitative and qualitative evaluation criteria indicate the superiority of the proposed method compared to other new methods of denoising and image quality enhancement, in preserving the details, texture of images and reducing the damage caused by the noise reduction filter.