Learning of Image Dehazing Models for Segmentation Tasks

Abstract

To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth. Despite its ability to produce visually good images, using pixel-based or even perceptual metrics does not guarantee, in general, that the produced image is fit for being used as input for low-level computer vision tasks such as segmentation. To overcome this weakness, we are proposing a novel end-to-end approach for image dehazing, fit for being used as input to an image segmentation procedure, while maintaining the visual quality of the generated images. Inspired by the success of Generative Adversarial Networks (GAN), we propose to optimize the generator by introducing a discriminator network and a loss function that evaluates segmentation quality of dehazed images. In addition, we make use of a supplementary loss function that verifies that the visual and the perceptual quality of the generated image are preserved in hazy conditions. Results obtained using the proposed technique are appealing, with a favorable comparison to state-of-the-art approaches when considering the performance of segmentation algorithms on the hazy images.

Input Dropout for Spatially Aligned Modalities

“…Over the RGB-only baseline, relative improvements of 3.6% and 3.4% on PSNR and SSIM respectively are observed when using Input Dropout on RGB+D, and 4.5% PSNR and 2.2% SSIM for RGB+S. We also compare our method to competing techniques, such as "Dehazing for segmentation" (D4S) [5] which proposes an approach to dehaze to increase performance for a subsequent task using a modality only available during training, and Pix2Pix GAN [3] which employs an extra generator to generate the missing modality from the RGB image. In every case, Input Dropout performs better while being simpler than the other approaches.…”