3 research outputs found
Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images
The new alternative is to use deep learning to inpaint any image by utilizing
image classification and computer vision techniques. In general, image
inpainting is a task of recreating or reconstructing any broken image which
could be a photograph or oil/acrylic painting. With the advancement in the
field of Artificial Intelligence, this topic has become popular among AI
enthusiasts. With our approach, we propose an initial end-to-end pipeline for
inpainting images using a complete Machine Learning approach instead of a
conventional application-based approach. We first use the YOLO model to
automatically identify and localize the object we wish to remove from the
image. Using the result obtained from the model we can generate a mask for the
same. After this, we provide the masked image and original image to the GAN
model which uses the Contextual Attention method to fill in the region. It
consists of two generator networks and two discriminator networks and is also
called a coarse-to-fine network structure. The two generators use fully
convolutional networks while the global discriminator gets hold of the entire
image as input while the local discriminator gets the grip of the filled region
as input. The contextual Attention mechanism is proposed to effectively borrow
the neighbor information from distant spatial locations for reconstructing the
missing pixels. The third part of our implementation uses SRGAN to resolve the
inpainted image back to its original size. Our work is inspired by the paper
Free-Form Image Inpainting with Gated Convolution and Generative Image
Inpainting with Contextual Attention
Image Restoration from Parametric Transformations using Generative Models
When images are statistically described by a generative model we can use this
information to develop optimum techniques for various image restoration
problems as inpainting, super-resolution, image coloring, generative model
inversion, etc. With the help of the generative model it is possible to
formulate, in a natural way, these restoration problems as Statistical
estimation problems. Our approach, by combining maximum a-posteriori
probability with maximum likelihood estimation, is capable of restoring images
that are distorted by transformations even when the latter contain unknown
parameters. The resulting optimization is completely defined with no parameters
requiring tuning. This must be compared with the current state of the art which
requires exact knowledge of the transformations and contains regularizer terms
with weights that must be properly defined. Finally, we must mention that we
extend our method to accommodate mixtures of multiple images where each image
is described by its own generative model and we are able of successfully
separating each participating image from a single mixture
Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention
Deep learning (DL) has demonstrated its powerful capabilities in the field of
image inpainting. The DL-based image inpainting approaches can produce visually
plausible results, but often generate various unpleasant artifacts, especially
in the boundary and highly textured regions. To tackle this challenge, in this
work, we propose a new end-to-end, two-stage (coarse-to-fine) generative model
through combining a local binary pattern (LBP) learning network with an actual
inpainting network. Specifically, the first LBP learning network using U-Net
architecture is designed to accurately predict the structural information of
the missing region, which subsequently guides the second image inpainting
network for better filling the missing pixels. Furthermore, an improved spatial
attention mechanism is integrated in the image inpainting network, by
considering the consistency not only between the known region with the
generated one, but also within the generated region itself. Extensive
experiments on public datasets including CelebA-HQ, Places and Paris StreetView
demonstrate that our model generates better inpainting results than the
state-of-the-art competing algorithms, both quantitatively and qualitatively.
The source code and trained models will be made available at
https://github.com/HighwayWu/ImageInpainting