Ke Xu

TL;DR: A semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images is proposed, and a novel SPatial Attentive Network (SPANet) is proposed to remove rain streaks in a local-to-global manner.

TL;DR: A frequency-based decompositionand- enhancement model that first learns to recover image objects in the low-frequency layer and then enhances high-frequency details based on the recovered image objects and outperforms state-of-the-art approaches in enhancing practical noisy low-light images.

TL;DR: Wang et al. as mentioned in this paper proposed a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images.

TL;DR: Zhang et al. as discussed by the authors formulated the image correction task as an HDR transformation process and proposed a novel approach called Deep Reciprocating HDR Transformation (DRHT), which first reconstruct the missing details in the HDR domain and then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details.

TL;DR: In this article, a deep recurrent fusion network (DRFN) was proposed, which utilizes transposed convolution instead of bicubic interpolation for upsampling and integrates different-level features extracted from recurrent residual blocks to reconstruct the final HR images.