Considering the problem that traditional image enhancement methods based on a normalized incomplete beta function (NIBF) have difficultly obtaining optimal parameters automatically and that enhancement effects are limited by the dynamic range of the image, a method of NIBF remote sensing image automatic enhancement based on an adaptive quantum genetic algorithm was proposed. First, from the image color depth, the maximum and minimum spectral measurement levels were introduced into the image to be enhanced to expand its dynamic range. Secondly, the parameters of NIBF were encoded into quantum chromosomes using quantum bits, and several quantum chromosomes were set as the initial parameter population. The parameter population was measured and decoded, the decoded value was input as a parameter of NIBF, and the image was transformed by spectral measure to obtain the corresponding enhanced image population. Then, edge images of each individual in the enhanced image population were extracted using the eight-direction edge detection template. The fitness function of individual quality in the parameter population was defined by edge intensity, edge number, and entropy measure, and each parameter in the parameter population was evaluated and retained, the best parameters of individuals were recorded. In the proposed evolutionary strategy, the quantum rotation gate was used to evolve the quantum chromosomes toward to the direction of maximum fitness level, and the size of the quantum rotation angle was adaptively adjusted according to the difference of each generations fitness and evolutionary algebra. The best parameters of NIBF were the individuals with the most fitness in the finally evolved parameter population, and the corresponding spectral measure transformation curve was generated to determine the mapping relationship between the input and output spectral measure, so optimal automatic enhancement of the image was achieved. The blind/referenceless image spatial quality assessment indicators increase by 122.2%, the natural image quality assessment indicators increased by 71.8%, and the running time is 10.758 s. The proposed algorithm satisfies the requirements of automation, robustness, and high efficiency in remote sensing image enhancement.