Enhancement of Asymmetrically Color-Cast Sandstorm Image Using Saturation-Based Color Correction and Hybrid Transmission Network

Abstract

The images discussed in this manuscript show atmospheric conditions of smog, sandstorm, and dust. Moreover, the images were taken in various environments and have features such as dimness or color cast. The smoggy image has a greenish or bluish color veil, and the sandstorm image has a yellowish or reddish color veil because of the various sand particles. Various methods have been used to enhance images containing dust. However, if the color-cast ingredients are not considered during image enhancement, then the enhanced image will have a new, artificial color veil that did not appear in the input image, as the color-veiled image does not have a uniform color channel. Certain channels are attenuated by sand particles. Therefore, this paper proposes a color-balancing method based on saturation to enhance asymmetrically cast colors due to the attenuation of the color channel by sand particles. Moreover, because the balanced image contains dust and the distribution of hazy ingredients is asymmetrical, a dehazing procedure is needed to enhance the image. This work used the original image and a reversed image to train the hybrid transmission network and generate the image’s transmission map. Moreover, an objective and subjective assessment procedure was used to compare the performance of the proposed method with that of other methods. Through the assessment, the performance of the proposed method was shown to be superior to other methods’ performance.

Keywords

• sandstorm image enhancement
• asymmetric color-channel compensation
• saturation-based color balancing
• hybrid transmission map
• convolutional neural network