Prior based Pyramid Residual Clique Network for human body image super-resolution

Abstract

Recent research in the analysis of human images, such as human parsing and pose estimation, usually requires input images to have a sufficiently high resolution. However, small images of people are commonly encountered in our daily lives, particularly in surveillance applications. This paper aims to ultra-resolve a tiny person image to its high-resolution counterpart by learning effective feature representations and exploiting useful human body prior knowledge. First, we propose the Residual Clique Block (RCB) to fully exploit compact feature representations for image Super-Resolution (SR). Second, a series of RCBs are cascaded in a coarse-to-fine manner to construct the Pyramid Residual Clique Network (PRCN), which simultaneously reconstructs multiple SR results (e.g. 2×, 4×, and 8×) in one feed-forward pass. Third, we utilize the human parsing map as the shape prior, and the high-frequency sub-bands of Uniform Discrete Curvelet Transform (UDCT) as the texture prior to enhance the details of reconstructed human body image. Experimental results demonstrate that our proposed method achieves state-of-the-art performance with superior visual quality and PSNR/SSIM scores. Moreover, we show that our results can considerably enhance the performance of human parsing and pose estimation tasks.