No-reference image quality assessment for dehazed images

Abstract

Traditional image quality prediction methods require the pristine image as a reference, such as structural similarity. However, it is difficult to provide a haze-free image as a reference when predicting the quality of the dehazed image. Therefore, it is necessary to use a no-reference image quality assessment (NR-IQA) method. In addition, most NR-IQA methods are based on known distortion type, using a large number of subjective opinion scores and images with the same distortion to train the model. We developed an innovative NR-IQA specifically for dehazed images without such prior knowledge. Since most images will undergo color distortion and blur after dehazing, it is proposed to combine color and sharpness for evaluation. The quality of the image is evaluated on the HSI color space, where the H and S channels are utilized to evaluate color, and the I channel to sharpness. Experimental results show that the performance of the proposed metric is better than other existing evaluation methods for dehazed images.