Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated conv...
详细信息
Video understanding is an important problem in computervision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single cl...
详细信息
As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For examp...
详细信息
Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most availab.e resources are still in standard dynamic range (SDR). Therefore, there is a...
详细信息
ISBN:
(纸本)9781665428132
Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most availab.e resources are still in standard dynamic range (SDR). Therefore, there is an urgent demand to transform existing SDR-TV contents into their HDR-TV versions. In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step solution pipeline including adaptive global color mapping, local enhancement and highlight generation. Moreover, the above analysis inspires us to present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping. In addition, we construct a dataset using HDR videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results achieve state-of-the-art performance in quantitative comparisons and visual quality. The code and dataset are availab.e at https://***/chxy95/HDRTVNet.
This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel att...
详细信息
Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be...
详细信息
Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertaint...
详细信息
ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertainties caused by ambiguous expressions, low-quality facial images, and the subjectiveness of annotators, which limits FER performance. To address this challenge, this paper introduces novel Rayleigh and weighted-softmax loss from two aspects. First, we propose Rayleigh loss to extract discriminative representation, which aims at minimizing within-class distances and maximizing inter-class distances simultaneously. Moreover, Rayleigh loss has a Euclidean form which make it easily be optimized with SGD and be combined with other forms. Second, we introduce a weight to measure the uncertainty of a given sample, by considering its distance to class center. Extensive experiments on RAF-DB, FERPlus and AffectNet show the effectiveness of our method with SOTA performance.
Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR...
详细信息
Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to signi...
详细信息
The quality of images captured in bad weather is often affected by chromatic casts and low visibility due to the presence of atmospheric particles. Restoration of the color balance is often ignored in most of the exis...
详细信息
ISBN:
(数字)9781728171685
ISBN:
(纸本)9781728171692
The quality of images captured in bad weather is often affected by chromatic casts and low visibility due to the presence of atmospheric particles. Restoration of the color balance is often ignored in most of the existing image de-hazing methods. In this paper, we propose a varicolored end-to-end image de-hazing network which restores the color balance in a given varicolored hazy image and recovers the haze-free image. The proposed network comprises of 1) Haze color correction (HCC) module and 2) Visibility improvement (VI) module. The proposed HCC module provides required attention to each color channel and generates a color balanced hazy image. While the proposed VI module processes the color balanced hazy image through novel inception attention block to recover the haze-free image. We also propose a novel approach to generate a large-scale varicolored synthetic hazy image database. An ablation study has been carried out to demonstrate the effect of different factors on the performance of the proposed network for image de-hazing. Three benchmark synthetic datasets have been used for quantitative analysis of the proposed network. Visual results on a set of real-world hazy images captured in different weather conditions demonstrate the effectiveness of the proposed approach for varicolored image de-hazing.
暂无评论