The quality of underwater images is affected by characteristics of the underwater environment, such as varying light intensity levels and varied wavelengths. Low quality of underwater images is one of the major problems in identification of fish species during monitoring of underwater ecosystem. Improving the quality of underwater images is important for accurate fish identification. Some researchers introduce various methods that address colour-correction problem for underwater images. However, previous researches do not consider the noises produced during the implementation of the image processing techniques. To deal with this problem, we propose a novel method called novel contrast-adaptive colour-correction (NCACC) to enhance the quality of underwater images that are susceptible to bright colour distortion and various noises. The NCACC method is a combination of an automatic level correction and a limited contrast mode of adaptive histogram equalization method to be applied to the dark channel prior method. As a result, we are able to improve the contrast of the images without generating many noises. The experimental results show that the NCACC method significantly improves the quality of the underwater images. The improvement was assessed using the peak signal-to-noise ratio that yielded the value of 22.076 dB, which was 4.4% higher than the highest value obtained by the state-of-theart method. We demonstrate that enhancement of underwater images is essential to reveal the detail of underwater objects.