BrGAN: Blur Resist Generative Adversarial Network With Multiple Joint Dilated Residual Convolutions for Chlorophyll Color Image Restoration

Ziyi Chen, Yuhua Luo, Yiping Chen, Jing Wang, Dilong Li, Kyle Gao, Cheng Wang, Jonathan Li

Abstract: This article presents a blur resist generative adversarial network (BrGAN) with multiple joint dilated residual convolutions for chlorophyll image restoration of the Geostationary Ocean Color Imager (GOCI). First, a publicly available dataset was built to support this study. Second, a multiple attention perception mechanism and a multiple joint dilated residual convolution module were proposed to cope with the challenge of large missing areas in GOCI chlorophyll images. Third, a patch generative adversarial network (GAN)-based discrimination module was proposed to avoid the restored areas with generating mosaic and shadows. Our experimental results demonstrate that the BrGAN can reach 37.06 in the peak signal-to-noise ratio (PSNR) and 0.0485 in the Learned Perceptual Image Patch Similarity (LPIPS), respectively. The comparative study shows that the BrGAN achieves the highest effectiveness and advancement among other seven state-of-the-art (SOTA) methods.