C2MSNet: A Novel approach for single image haze removal
This addresses image quality degradation due to haze, particularly in gloomy environments, for applications like computer vision, but it is incremental as it builds on existing deep learning methods.
The paper tackles single image haze removal by proposing a cardinal color fusion network that fuses color information to generate depth maps and estimates a transmission map using a multi-channel multi-scale CNN, achieving state-of-the-art performance on metrics like SSIM, MSE, and PSNR.
Degradation of image quality due to the presence of haze is a very common phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand crafted haze relevant features. However, these methods have the problem of color distortion in gloomy (poor illumination) environment. In this paper, a cardinal (red, green and blue) color fusion network for single image haze removal is proposed. In first stage, network fusses color information present in hazy images and generates multi-channel depth maps. The second stage estimates the scene transmission map from generated dark channels using multi channel multi scale convolutional neural network (McMs-CNN) to recover the original scene. To train the proposed network, we have used two standard datasets namely: ImageNet [5] and D-HAZY [1]. Performance evaluation of the proposed approach has been carried out using structural similarity index (SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR). Performance analysis shows that the proposed approach outperforms the existing state-of-the-art methods for single image dehazing.