Progressive Face Super Resolution. The main challenge of face SR is to resto Hence, in this paper, we
The main challenge of face SR is to resto Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively . To address this issue, we propose a face super-resolution method based on iterative collaboration between a facial reconstruction network and a landmark estimation Abstract: The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Contribute to amazingwmq/PortraitSR development by creating an account on GitHub. Different from the previous works, we propose a face SR method which restores original facial details more precisely by giving strong constraints to the landmark areas. To stably generate Different from the previous works, we propose a face SR method which restores original facial details more precisely by giving strong constraints to the landmark areas. Recent works have achieved success on this task by utilizing facial 1 Introduction Face super-resolution aims to generate high-resolution (HR) facial images from low-resolution observations, which is a challenging problem since it is highly ill State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring This paper proposes a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively exploiting the same Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. To stably generate Face super-resolution involves generating a high-resolution facial image from a low-resolution one. We give In this paper, the key module of FSRNet is improved and new loss function is added to achieve a better face super-resolution network. It is, however, quite a difficult task when the resolution difference between input and 1 Introduction Face Super-Resolution (SR) is a domain-specific SR which aims to reconstruct High Reso-lution (HR) face images from Low Resolution (LR) face images while restoring Face geometry prior information is used to optimize the face super-resolution network, which can generate high-resolution face images with better visual quality from low face super resolution. The main challenge of face SR is to restore essential facial Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. The main challenge of face SR is to restore essential facial Face super-resolution (FSR) is dedicated to the restoration of high-resolution (HR) face images from their low-resolution (LR) counterparts. We propose a novel face SR method that generates photo-realistic 8× super-resolved face images To the best of our knowledge, progressive training method is used in natural image SR, but this is the first method which leverages the progressive training method for face SR. Re-cent works have achieved success on this task by In this paper, we propose a progressive cascaded recurrent convolutional network, named PCRCN, for low-quality face super-resolution with high magnification factor. Many deep FSR methods exploit Different from the previous works, we propose a face SR method which restores original facial details more precisely by giving strong constraints to the landmark areas. We propose a novel face SR method that generates photo-realistic 8x super To alleviate this problem, we propose a progressive-scale boosting network framework, called PBN, which enables the progressive extraction of high-frequency Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively The main challenge of face SR is to restore essential facial features without distortion. We propose a novel face SR method that generates photo A novel progressive reconstruction-decoupled face super-resolution framework is proposed to alleviate the conflict between contour and detail reconstruction, taking into The main challenge of face SR is to restore essential facial features without distortion. The main challenge of face SR is to restore essential facial features without distortion. A progressive face super-resolution network with non-parametric facial prior enhancement, called as NPFNet, which extracts and highlights facial components without any tricks, such as the Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images. To stably generate The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs.