Image Super-Resolution using Explicit Perceptual Loss
- URL: http://arxiv.org/abs/2009.00382v1
- Date: Tue, 1 Sep 2020 12:22:39 GMT
- Title: Image Super-Resolution using Explicit Perceptual Loss
- Authors: Tomoki Yoshida and Kazutoshi Akita and Muhammad Haris and Norimichi
Ukita
- Abstract summary: We show how to exploit the machine learning based model which is directly trained to provide the perceptual score on generated images.
The experimental results show the explicit approach has a higher perceptual score than other approaches.
- Score: 17.2448277365841
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes an explicit way to optimize the super-resolution network
for generating visually pleasing images. The previous approaches use several
loss functions which is hard to interpret and has the implicit relationships to
improve the perceptual score. We show how to exploit the machine learning based
model which is directly trained to provide the perceptual score on generated
images. It is believed that these models can be used to optimizes the
super-resolution network which is easier to interpret. We further analyze the
characteristic of the existing loss and our proposed explicit perceptual loss
for better interpretation. The experimental results show the explicit approach
has a higher perceptual score than other approaches. Finally, we demonstrate
the relation of explicit perceptual loss and visually pleasing images using
subjective evaluation.
Related papers
- Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models [85.96013373385057]
Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent.
However, excessive optimization with such reward models, which serve as mere proxy objectives, can compromise the performance of fine-tuned models.
We propose TextNorm, a method that enhances alignment based on a measure of reward model confidence estimated across a set of semantically contrastive text prompts.
arXiv Detail & Related papers (2024-04-02T11:40:38Z) - Deep Semantic Statistics Matching (D2SM) Denoising Network [70.01091467628068]
We introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network.
It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space.
By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks.
arXiv Detail & Related papers (2022-07-19T14:35:42Z) - Two-stage Visual Cues Enhancement Network for Referring Image
Segmentation [89.49412325699537]
Referring Image (RIS) aims at segmenting the target object from an image referred by one given natural language expression.
In this paper, we tackle this problem by devising a Two-stage Visual cues enhancement Network (TV-Net)
Through the two-stage enhancement, our proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image.
arXiv Detail & Related papers (2021-10-09T02:53:39Z) - Inverting Adversarially Robust Networks for Image Synthesis [37.927552662984034]
We propose the use of robust representations as a perceptual primitive for feature inversion models.
We empirically show that adopting robust representations as an image prior significantly improves the reconstruction accuracy of CNN-based feature inversion models.
Following these findings, we propose an encoding-decoding network based on robust representations and show its advantages for applications such as anomaly detection, style transfer and image denoising.
arXiv Detail & Related papers (2021-06-13T05:51:00Z) - Regularization via deep generative models: an analysis point of view [8.818465117061205]
This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network.
In many cases our technique achieves a clear improvement of the performance and seems to be more robust.
arXiv Detail & Related papers (2021-01-21T15:04:57Z) - Projected Distribution Loss for Image Enhancement [15.297569497776374]
We show that aggregating 1D-Wasserstein distances between CNN activations is more reliable than the existing approaches.
In imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.
arXiv Detail & Related papers (2020-12-16T22:13:03Z) - Loss Bounds for Approximate Influence-Based Abstraction [81.13024471616417]
Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them.
This paper investigates the performance of such approaches from a theoretical perspective.
We show that neural networks trained with cross entropy are well suited to learn approximate influence representations.
arXiv Detail & Related papers (2020-11-03T15:33:10Z) - Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
Blind image restoration is a common yet challenging problem in computer vision.
We propose a novel blind image restoration method, aiming to integrate both the advantages of them.
Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
arXiv Detail & Related papers (2020-08-25T03:30:53Z) - Perceptually Optimizing Deep Image Compression [53.705543593594285]
Mean squared error (MSE) and $ell_p$ norms have largely dominated the measurement of loss in neural networks.
We propose a different proxy approach to optimize image analysis networks against quantitative perceptual models.
arXiv Detail & Related papers (2020-07-03T14:33:28Z) - Assessing the Reliability of Visual Explanations of Deep Models with
Adversarial Perturbations [15.067369314723958]
We propose an objective measure to evaluate the reliability of explanations of deep models.
Our approach is based on changes in the network's outcome resulting from the perturbation of input images in an adversarial way.
We also propose a straightforward application of our approach to clean relevance maps, creating more interpretable maps without any loss in essential explanation.
arXiv Detail & Related papers (2020-04-22T19:57:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.