Poisson Image Editing

Martino, J. Matías Di; Facciolo, Gabriele; Meinhardt-Llopis, Enric

doi:10.5201/ipol.2016.163

Cited by 40 publications

(23 citation statements)

References 27 publications

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“…Figure 6(b) that the color of the replaced face is slightly different from the neck, ears, forehead, and other residual parts of the reference face. In order to eliminate the boundary effect and make the composite face more realistic, we use generic interpolation machinery based on solving Poisson equations [19,20] to correct the color of the replaced face and smooth the boundary. The main idea of the color correction algorithm is minimizing the color difference between the original pixels and the replaced ones while preserving the gradient of pixels at the boundary.…”

Section: Methodsmentioning

confidence: 99%

Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

Chen

et al. 2019

Mathematical Problems in Engineering

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We propose an image-based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face-ROIs, and then the face-ROI in the reference image is replaced with the input face-ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect.

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Section: Methodsmentioning

confidence: 99%

Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

Chen

et al. 2019

Mathematical Problems in Engineering

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“…The Post-processing module involves 3 primary substeps which are 1) applying gain compensation [11] operation on O'' image, 2) performing Modified-poisson-blending operation [24], [49]- [51] on the gain-compensated O'' image, 3) finally applying texture correction operations for edge refinement & image fine-tuning [24], [31]. The l,r-PanoED network's output O'' image contains irregular illumination patches near the stitched areas(generally in the center of the stitched image) (Fig.…”

Section: Post-processing Modulementioning

confidence: 99%

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Chilukuri

Padala

Padala³

et al. 2021

IEEE Access

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Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & majordisadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each submodule is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoEDnetwork) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-featuremaps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & featuremapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L α,β,γ , FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.INDEX TERMS Deep feature extraction, encoder-decoder cnn, image mosaicing, multi-image registration, non-homogeneous image stitching.

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“…In recent years, landmark detection technology has gradually matured, and many open-source models have been used. However, when the image's resolution is lower than 30 pixels, the detection accuracy of medium and large poses face at standard facial resolution (192 × 192) has decreased by 15% and 30%, respectively [16]. Therefore, the existing research on facial landmark detection mainly focuses on improving ultra-low resolution facial images' detection accuracy.…”

Section: Related Work a Facial Landmark Detectionmentioning

confidence: 99%

“…After the posture and expression are normalized, it is necessary to adjust further the landmarks of the boundary of the distorted image to eliminate the change in the position of the landmarks and preserve the original structure of the image. Finally, through the light adaptation [21] and edge filling [27] the image artifacts caused by the large yaw angle compensated and final distorted image I wg is obtained.…”

Section: B the Pose Deformation Modulementioning

confidence: 99%

Guided Cascaded Super-Resolution Network for Face Image

Cao

Liu

et al. 2020

IEEE Access

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The image super-resolution algorithm can overcome the imaging system's hardware limitation and obtain higher resolution and clearer images. Existing super-resolution methods based on convolutional neural networks(CNN) can learn the mapping relationship between high-resolution(HR) and low-resolution(LR) images. However, when the reconstruction target is a face image, the reconstruction results often have problems that the face area is too smooth and lacks details. We propose a guided cascaded face super-resolution network, called guided cascaded super-resolution network (GCFSRnet). GCFSRnet takes the LR image and a high-quality guided image as inputs, and it consists of a pose deformation module and a super-resolution network. Firstly, the pose deformation module converts the guide image's posture into the same as the low-resolution face image based on 3D fitting and 3D morphable model (3DMM). Then, the LR image and the deformed guide image are used as input of the super-resolution network. The super-resolution networks are formed by a cascade of two layers of networks, which extract different features. During the reconstruction process, the guide image can provide real facial details and help generate subtle facial textures. The cascade structure of a super-resolution network can gradually extract features and restore different levels of image details. The experimental results on the CASIA Web Face and CelebA datasets show that the proposed method can generate facial images with clear outlines and rich details, which are superior to other state-of-the-art methods such as SRResNet, SRGAN, VDSR, DBPN, etc.

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Poisson Image Editing

Cited by 40 publications

References 27 publications

Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Guided Cascaded Super-Resolution Network for Face Image

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