# Photorealistic Style Transfer

Bhagyashree Zala, Bhavya Shah, Usha Veeramachaneni

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Bhagyashree Zala, Bhavya Shah, Usha Veeramachaneni. Photorealistic Style Transfer. University of Alberta. 2022. hal-03904620

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# Photorealistic Style Transfer

Bhagyashree Zala, Bhavya Shah, Usha Veeramachaneni

University of Alberta

December 17, 2022

## Abstract

Photorealistic style transfer refers to the process of transferring the style of one image to another image, while still maintaining a high level of visual realism. This is typically done using machine learning algorithms that are trained on a dataset of images with known styles. The resulting images typically have the same content as the original image, but with the added style of the reference image. This technique can be used to create a wide range of interesting visual effects, from adding a painterly style to a photograph to creating entirely new works of art. In this paper, We will use a machine learning program to synthesize the content image to a style image without structural distortions by using a style image to obtain the transferred style and a stylized result. This project can demonstrate how well machine learning techniques perform in image processing and how artificial intelligence can, to some extent, achieve considerable success in the field of art and creativity. Additionally, we suggest a linear propagation module allowing a feed-forward network to send photos with a realistic appearance.

## 1. Introduction

Deep learning-based style transfer has been studied to match statistical information between content and style images based on features extracted from previously trained convolutional neural networks. One main drawback of the method by **Gatys et al.** [7] is the high computational cost due to the iterative optimization process. Fast feed-forward approaches address this issue by training feed-forward neural networks that minimize the same feature and style reconstruction loss. However, each feed-forward network is trained to transfer one fixed style. **Dumoulin et al.** [17] introduce an instance normalization layer that allows 32 styles represented by one model. **Li et al.** [21] encode 1,000 styles using a binary selection unit for image synthesis. Nevertheless, these models cannot transfer an arbitrary style onto a content image.

[20]Several methods have been proposed to match the mean and variance of content and style using feature vectors to transfer an arbitrary style onto a content image. But, these methods do not model the covariance of features and may not synthesize images sufficiently.
**Li et al.** [22] resolve this problem by applying whitening and coloring transform with pre-trained image reconstruction auto-encoders. [13] Although, this approach is computationally expensive due to the need for large matrix decompositions at multiple levels. **Shen et al.** [14] propose to train a meta-network that generates a 14-layer network for each content and style image pair. However, it requires extra memory for each content/style pair and does not explicitly model the second-order image statistics.

## 2. Goals and Objectives

We aim to implement a framework for analyzing universal style transfer methods and present a practical and efficient algorithm by learning linear transformations. Also, a linear propagation module enables a feed-forward network for photo-realistic style transfer.

By using linear style transfer combined with **spatial propagation networks (SPNs)** can reduce distortions caused by the auto-encoder to achieve photo-realistic style transfer. As in **Liu et al.** [13] the SPN consists of a CNN with eight convolution layers that produce local weights that form a pixel-affinity matrix and a linear propagation layer that creates filtered images.

Later the style transfer encoder-decoder is fixed at the training stage, and a reconstruction image generated by the auto-encoder is fed into the propagation module as input. Under the guidance of the corresponding whitened image, the propagation module produces a refined reconstruction image. Then we compute the qualitative comparison between the refined and original images. Furthermore, our objective is to deliver an effective algorithm that is computationally efficient, flexible for numerous tasks, and effective for stylizing images.

## 3. Responsibilities

<figure><img src="image_1.png" /><figcaption>Figure 1: Responsibilities of individual</figcaption></figure>
## 4. Milestones

<figure><img src="image_2.png" /><figcaption>Figure 2: Project Milestone</figcaption></figure>

## 5. Literature Review

### 5.1 A Neural Algorithm of Artistic Style

In this paper, they introduced a synthetic system that produces creative images of excellent perceptual quality using a Deep Neural Network. The system separates objects using neural representations. [8] Then blend the style and content of random images to create a neural network algorithm for producing creative photos. As a final outcome, The VGG-Network, a Convolutional Neural Network was established and was used as the foundation for the results reported in the main text. This network rivals human performance on a typical benchmark task for visual object recognition. The 16 convolutional layers and 5 pooling layers of the layer VGG Network provided the feature space that they employed.

### 5.2 Deep Photo Style Transfer

They presented a deep learning method for photographic style transfer in this work. [2] They acknowledged that the output of this algorithm still has issues and is not what was anticipated, but they attempted to address these issues by requiring that the transformation from
the input to the output be locally affine in colorspace and expressing this requirement as a
specific fully differentiable energy term. They essentially outperform distortion.

## 5.3 Image Style Transfer Using Convolutional Neural Networks

In this paper, they claimed that rendering an image's semantic content in many visual styles
may be challenging. The absence of image representations that directly convey semantic
information and enable the separation of image content from style may be argued to be a
significant limiting factor for earlier techniques. [9] In this case, they make high-level visual
information explicit by using image representations created by convolutional neural networks
that are tuned for object detection. They present A Neural Algorithm of Artistic Style, which
can deconstruct and reconstitute the style and content of real photographs.

## 5.4 Background Modeling by Stability of Adaptive Features in Complex Scenes

In this paper, they mentioned that earlier methods of background removal in freely moving
camera usually concentrate on increasing the precision of motion estimate. In this study,
they suggest that the incorporation of several foreground- and background-related cues make
precise background subtraction achievable. [24] In their result, the foreground is segregated
by merging superpixels with closeness under several levels, where proximity is the outcome
of competition between foreground and background cues in a certain superpixel. Compre-
hensive analyses utilizing accepted benchmarks show how much better their work is than
the state-of-the-art.

## 5.5 Background Subtraction Based on Integration of Alternative Cues in Freely Moving Camera

In this research, they offered the stability of the adaptive feature (SoAF) model, a unique
multi-feature-based background model that uses the stabilities of various features in a pixel
to adaptively weigh the contributions of these features to foreground recognition. [3] They
have done this mostly because background pixels' characteristics are frequently more consis-
tent.

## 5.6 Background Subtraction by Difference Clustering

In this paper, they have said that background subtraction techniques often viewed the issue
as a classification of pixels across time. By framing the issue as a clustering of the difference
vectors between pixels in the current frame and the background image set, they introduced
Difference Clustering, a background subtraction technique that reduces computing time and
produces accurate results with high Pr and Fm values. [19] In this research, they suggested
the difference clustering method for different backgrounds. They utilized a novel approach
to cluster data as opposed to the conventional k-means techniques.

## 5.7 Fog Removal of Aerial Image Based on Gamma Correction and Guided Filtering

In this paper, a combination of Gamma correction and Retinex defogging algorithm is sug-
gested for aerial foggy photos in order to enhance the pilots' perception of the runway and
the surrounding objects on foggy days and improve the visual effect of aerial photographs. In
conclusion, a more contrast-rich and crisp auxiliary vision system is required. [18] This re-
search suggests an enhanced Retinex algorithm to increase contrast based on the properties
of aerial images. The revised method suggested in this work may successfully remove the
aerial fog, increase the image contrast, and retain consistency in colour, according to the
analysis and comparison of the experimental findings.

5.8 Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer

In this paper, they proposed a novel framework called Deep Translation Prior (DTP) that enables photorealistic style transfer through test-time training on a pair of input images using untrained networks, which improves performance and generalization by learning an image pair-specific translation prior. Because the networks were only to be learned during testing, their approach doesn't require an offline training step for style transfer, which has been one of the major problems with prior methods. [5] Their experimental findings show that their framework works even better than state-of-the-art approaches and has a better generalization ability to unknown image pairs.

5.9 High-Resolution Network for Photorealistic Style Transfer

In this paper, the image-generating network is a high-resolution network. Their generation
network maintains high resolution throughout the process as opposed to other techniques
that first reduce resolution before restoring it. High-resolution subnets can continually
receive data from low-resolution subnets by connecting high-resolution and low-resolution
subnets in parallel and frequently multi-scale fusion. [12] In conclusion, they created a
high-resolution generation network. The neural style transfer approach employing the high-
resolution network gets good results in photorealistic style transfer with a structure and less
image distortion by connecting many subnets in parallel and repeating multi-scale fusion.

5.10 Photorealistic Style Transfer via Wavelet Transforms

This paper proposed a wavelet-corrected transfer based on whitening and coloring transforms (WCT2) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. They first showed that the spatial distortions come from the network operations that cannot satisfy the reconstruction condition. Their model removes the additional post-processing steps by employing the wavelet-corrected transfer and progressive stylization. [4] The model can process a high-resolution image (1024×1024) in 4.7 seconds, which is 830 times faster than the state-of-the-art models, where PhotoWCT fails due to an out-of-memory issue, and Deep Photo Style Transfer (DPST) takes 3887.8 seconds. Their experimental results showed quantitatively better visual quality in both SSIM and Gram loss and qualitatively being preferred by 62.21

5.11 Photorealistic Style Transfer with Screened Poisson Equation

This paper proposed a two-stage framework for photorealistic style transfer, starting with
style transfer and then post-processing to refine the photorealism. The post-processing stage
takes a stylized image and manipulates it, making its appearance more photorealistic, using
information from the original input image. In total, the paper had three key contributions.
First, they produced photorealistic and natural-looking results by constraining the manip-
ulation with the original image gradients. Second, in comparison to the outstanding work
of Luan et al., their approach is much faster, taking less than 2 seconds to run. Finally, it
relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Screened Poisson Equation (SPE) was used for image filtering. One limitation of SPE concerning DPST [2] is dealing with significant contrast inversion; SPE tends to create 'halo' artifacts around firm edges.

## 5.12 Ultrafast Photorealistic Style Transfer via Neural Architecture Search

Most proposed photorealistic style transfer algorithms must rely on the post or pre-process to make the generated images look photorealistic. If they disable the additional processing, these algorithms will fail to produce plausible photorealistic stylization in detail preservation and photorealism. This paper proposes an effective solution to these issues. The model consists of a construction step (C-step) to build a photorealistic stylization network and a pruning step (P-step) for acceleration. In the C-step, we propose a dense auto-encoder named PhotoNet based on a carefully designed pre-analysis. PhotoNet integrates a feature aggregation module (BFA) and Instance normalized skip links (INSL). [15] They adopted a neural architecture search method in the P-step to accelerate PhotoNet. An automatic network pruning framework for teacher-student learning for photorealistic stylization was used. The network architecture named PhotoNAS resulted from the search, achieving significant acceleration over PhotoNet while keeping the stylization effects almost intact. The proposed algorithm accomplishes better performance without any pre- or post-processing.

<figure><img src="image_3.png" /><figcaption>Figure 3: Photorealistic Stylization Network</figcaption></figure>

## 5.13 Photorealistic Style Transfer via Adaptive Filtering and Channel Separation

The problem of color and texture distortion remains unsolved in the photorealistic style transfer task. It is mainly caused by the interference between color and texture during transfer. This paper proposed an end-to-end network via adaptive filtering and channel separation to address this problem. [11] They utilized the adaptive weighted least squares filter (AWLSF) to smooth both the content and reference images, perform the color transformation in their structure layers, extract accurate textures via a feature extraction module, and finally obtain the photorealistic style transfer result by merging the module. The model utilized the WLS filter to extract the structure layers of content and reference images. To avoid the
mutual interference of the R, G, and B channels, they performed style transfer in the R, G, and B channels to achieve color transfer of the structure layer. The model utilized semantic segmentation in facial images to obtain a more accurate photorealistic style transfer effect.

## 5.14 Textureless Object Recognition Using an RGB-D Sensor

This paper proposed an effective and real-time system using a single RGB-D camera to recognize the industrial objects placed at arbitrary viewing directions around the vertical axis. Shape analysis techniques like shape representation and shape description are used in this model. [1] A shape representation method is a graph where the essential attributes of the shape are preserved. On the other hand, shape description methods provide a shape descriptor, commonly called a feature vector, represented by numerical data. [10] This framework has two major components: object detection and object recognition. The RGB input image is initially transformed during object detection into a grayscale image. Then, the image is segmented into background and foreground regions using a watershed segmentation approach. Morphological operations are applied later to remove noise. [6] For object recognition, we train several models to predict the different object classes based on a set of features extracted from the segmented regions. The experimental results demonstrated that this approach could distinguish between similar objects and is suitable for real-time robotics applications.

## 5.15 Human Body Fall Recognition System

This paper proposed a method to detect a fall in real-time. The proposed detection method consists of three stages: Video analysis, Body Recognition, and Trigger Alert. They carried out our research by implementing the Le2i data set. The Le2I data set is created to assess human falls in different surroundings. This data set includes various scenarios of human activities not limited to a simple fall, such as sitting, resting, and bending. The first step is to convert the video into frames. To ensure the best results for background subtraction, they used two methods: GMG and MOG2. These background subtractors are triggered based on the motion estimation and illumination threshold. After pre-processing the frame and selecting the Region of Interest (ROI), the human body is approximated with a bounding box and an ellipse. A fall is defined based on the parameter updates of the bounding box and ellipse. When the angle between the central axis and floor and the size of the ellipse or the ratio of width to height of the bounding box exceeds the threshold, the event is considered a fall. [23] The last phase of this system is the notification system, in which a notification is sent to the concerned person (guardian/caregiver) whenever the fall is detected.

## 5.16 Background subtraction based on deep pixel distribution learning

Background subtraction is essentially the categorization of pixels in a series of image frames, often under the premise of a static camera. This is challenging when dealing with varied and complicated situations. The intricacy and diversity of natural landscapes, such as rippling water, spouting fountains, and shadow, all present issues for background subtraction. The proposed framework in this research can produce promising outcomes in a variety of natural situations, and a thorough assessment against industry standards shows that our work is superior to state-of-the-art techniques.
<figure><img src="image_4.png" /><figcaption>Figure 4: Frame Processing step-by-step</figcaption></figure>

## 5.17 Universal Background Subtraction Based on Arithmetic Distribution Neural Network

Deep learning network-based complex models have recently obtained excellent performance, but there are still a few issues that prevent them from being used in practical background subtraction applications. Neuroscientists have developed a framework for learning the distributions of temporal pixels based on the Arithmetic Distribution Neural Network (ADNN). The input consists of histograms of subtractions between the present pixel and its previous equivalents. Calculating the distribution of random variables that follow specific distributions is done using arithmetic distribution techniques. This methodology has three advantages over other methods based on deep learning networks: a) works for unseen videos; b) results are produced using very few ground-truth frames; and c) one network can be trained for all testing videos even from various datasets.

## 5.18 Learning Linear Transformations for Fast Image and Video Style Transfer

Style transfer techniques include the Gatys et al. [9] approach, the AdaIn method, and the WCT [22] algorithm. These methods compute these matrices directly from deep feature vectors. As a result, these techniques do not offer broad solutions to this issue. The enormous dimensionality of deep feature vectors makes such matrix computation-based approaches computationally expensive. An effective linear propagation module for photo-realistic style transfer is presented in this paper. A single feed-forward network can construct the whole pipeline, which is quicker and more GPU-friendly than other approaches. This method is effective for stylizing photos and videos, efficient in terms of processing, and adaptable for a wide range of jobs.

## 5.19 Improving Picture Quality with Photo-Realistic Style Transfer

The authors of [16] described a model that, when compared to [8], operates more quickly and with higher quality. Random textures from the style may occasionally be pasted to the content image, but this does not affect the overall learning stage of the software. A style transfer algorithm with a quality loss function is proposed, which may be used for photo-based style transfer. That method made the tests non-scalable because the input images were chosen based on how similar they were. Expanding the test sample area without sacrificing quality is crucial.
## 5.20 Semantic Learning for Image Compression (SLIC)

Lossless compression reduces file size without sacrificing image quality, while lossy compression results in some quality loss from the original image. The trade-off between the degree of distortion and the compression ratio can be made more advantageous by using neural networks to optimize data compression. The Joint Photographic Experts Group (JPEG) is the most widely used lossy compression system. The proposed model can achieve compression ratios that are superior while retaining a comparable degree of visual quality as well as can detect many objects at any scale, and can be used to conduct variable-quality image compression.

## 5.21 Background Subtraction Based on Principal Motion for a Freely Moving Camera

When videos are obtained with a moving camera, it is more difficult to remove the background. Since the entire background moves as a function of camera motion, there are still very few techniques that show promise. Zhao [25] used a Deep Pixel Distribution Learning (DPDL) model to solve the problem. A novel method for eliminating background from video taken by moving cameras uses robust principal component analysis (RPCA) to examine the motion of video background where an optical flow is captured for the pixel depiction of motion as well as the pixel representation of motion. The technique involves analyzing the neighborhood information between individual pixels and their labels.

# 6. Methods

Style transfer relies on separating the content and style of an image. In order to produce a new target image with the appropriate content and style components, we require a content image and a style image. The target image contains the edges of objects and their structural arrangement from the content image and style, colors, and textures from the style image. For instance, check out the below images. The target image has style features from the style image by preserving its content features and it looks well blended.

<figure><img src="image_5.png" /><figcaption>Content Image</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Style Image</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Target Image</figcaption></figure>

## 6.1 Resnet34

ResNet34 is a convolutional neural network (CNN) architecture which is a type of deep learning model that is trained to recognize patterns and features in images and is widely used in computer vision tasks such as image classification, object detection, and segmentation. In our context, ResNet34 is used as the basis for a style transfer model. Specifically, we used a pre-trained ResNet34 model as the starting point for your style transfer model, and then fine-tune the model on a dataset of images that were paired with reference style images. This allowed us to leverage the pattern recognition capabilities of the ResNet34 model in order to transfer the style of the reference images to the content images.

In our code, we used the following layers of resnet34 to transfer the style from the content image into the content of the content image. A function to get the features were made with the selective layers of the resnet 34 architecture. Deeper layers focus more on the content of the image. Layer 3 Block 5 Convolution 2 (conv4-2) is used to preserve the content feature from the content image.

## 6.2 Gram Matrix

The output of every convolutional layer is a Tensor. After extracting the features from the content as well as style images, the gram matrix is used to transfer the style for more effective style feature extraction. In order to calculate the gram matrices for each layer in style representation, firstly, obtain the depth(d), height(h), width(w), and batch size. Secondly, Reshape that tensor so that the spatial dimensions are flattened; it Returns a new tensor with the same data as the self tensor but of a different shape. Finally, calculate the gram matrix by multiplying the reshaped tensor by its transpose for each layer in style representation.
## 6.3 Loss and Weigths

### Individual Layer Style Weights

We have weighted the style representation at each relevant layer and used a range between 0-1 to weight these layers. By weighting earlier layers ( conv1-1, conv2-1) more, we got larger style artifacts in our resulting, target image. If we choose to weigh later layers, we would have got more emphasis on smaller features. This is because each layer is a different size and together they create a multi-scale style representation!

### Content and Style Weight

We also defined an alpha (content weight) and a beta (style weight). We considered content-weight = 1 and modified the style-weight ratio.

### Updating the Target and Calculating Losses

We decided to go with 2000 iterations and inside the loop, we calculated the content and style losses and update your target image.

### Content Loss

The content loss will be the mean squared difference between the target and content features at layer conv4-2

$$ \text{content-loss} = \text{torch.mean}((\text{target-features}[\text{'conv4-2'}]\text{content-features}[\text{'conv4-2'}])^{**2}) $$

### Style Loss

The style loss is calculated in a similar way, only you have to iterate through a number of layers, specified by name in our dictionary style-weights. We calculated the gram matrix for the target image, target gram, and style image style-gram at each of these layers and compare those gram matrices, calculating the layer-style-loss. Later, you will see that this value is normalized by the size of the layer.

### Total Loss

Finally, we created the total loss by adding up the style and content losses and weighting them with our specified alpha and beta. Intermittently, we'll print out this loss; don't be alarmed if the loss is very large. It takes some time for an image's style to change and you should focus on the appearance of your target image rather than any loss of value. Still, you should see that this loss decreases over some number of iterations.
# 7. Discussion

ResNet (short for "Residual Network") is a type of convolutional neural network (CNN) that has been widely used for tasks such as image classification and object detection. In the context of photorealistic style transfer, ResNet could be used as part of the machine learning algorithm that is trained to perform the style transfer. Specifically, ResNet could be used as the network architecture for the CNN, which would be responsible for learning the mapping between the style of the reference image and the resulting stylized output image. This would allow the network to learn and apply complex styles to the input images, resulting in more photorealistic stylized outputs.

A few reasons why we used ResNet are stated below. Only two pooling layers are used throughout the network one at the beginning and the other at the end of the network. Identity connections are between every two CONV layers. Whereas, in VGG 19 network, after every convolution network there is a pooling layer that decreases accuracy and increases execution time. Along with that, ResNet architecture makes use of shortcut connections to solve the vanishing gradient problem. The basic building block of ResNet is a Residual block that is repeated throughout the network. As a result, ResNets are an efficient Neural Network Architecture, helping to maintain a low error rate deeper in the network. Thus, demonstrated to perform incredibly well in applications that need deep neural networks, such as feature extraction.

# 8. Results

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

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