Existing deep inpainting methods often generate contents with blurry textures and distorted structures, which manifests to well fuse the high-level structures and the low-level textures remains challenging. Meanwhile, current inpainting networks just process features at the encoder side but neglect the strong generative abilities in decoder pathway. To address above challenges, we propose a novel Dual Path Cross-Scale Attention Network (DPCSAN). It adopts U-Net architecture as backbone. Based on contextual attention, a Cross-Scale Attention Module (CSAM) is designed to transfer high-level structure information to adjacent low-level features across scales. CSAMs are equipped between each encoder layer and the symmetric decoder layer at multiple levels. The purpose of this design is to fill holes in shallower feature maps and to refine features generated by upsampling (with higher resolution). Experiments on Facade and CelebA-HQ demonstrate that our model performs better than state-of-the-art methods, while generating richer details and keeping original structures.

The images captured in low-light conditions are often of poor visual quality as most of details in dark regions buried. Although some advanced low-light image enhancement methods could lighten an image and its dark regions, they still cannot reveal the details in dark regions very well. This paper presents an adaptive dark region detail enhancement method for low-light images. As our method is based on the Retinex theory, we first formulate the Retinex-based low-light image enhancement problem into a Bayesian optimization framework. Then, a dark region prior is proposed and an adaptive gradient amplification strategy is designed to incorporate this prior into the illumination estimation. The dark region prior, together with the widely used spatial smooth and structure priors, leads to a dark region and structure-aware smoothness regularization term for illumination optimization. We provide a solver to this optimization and get final enhanced results after post processing. Experiments demonstrate that our method can obtain good enhancement results with better dark region details compared to several state-of-the-art methods.

It is not always easy for amateur photographers to capture photos with desired colors even on a classic hot spot as the appearance of color photo dependent on many factors. This paper proposes a novel approach to recolor given photos via a crowdsourcing based makeover scheme. When a user input a photo to be recolored, the proposed system will first conduct favorite exemplars suggestion from the images hosted by the social media sites, by jointly leveraging contextual and visual information associated with the images. The recommended exemplars shall reveal the scene and context dependent color compositions and provide users with diverse possible color styles. Then, a novel superpixel-based recoloring scheme, incorporating color statistics, texture characteristics and spatial constraints into soft matching, is applied to generate new photos of desired color. Experiments and a user study demonstrate that the proposed color photo makeover is able to achieve robust recoloring results for various outdoor photos.