Chromatic degradation is often caused by multiple factors that are difficult to retrieve after images have been acquired, making scene recovery a highly ill-posed inverse problem. In this paper, we leverage the nature of human vision and propose an effective algorithm to achieve robust and visually appealing reconstructions. Specifically, we develop a reliable method for estimating chromatic bias by averaging colors from large, blurred regions identified through scale-space analysis. We then utilize CIELAB perceptual hue similarity to determine the degree of degradation caused by transmission and backscattering. By combining these estimators, we recover the scene by adaptively discounting the bias across the image domain. We conduct numerical experiments and comparative studies to demonstrate the effectiveness of our method.