27 research outputs found
Towards practical deep learning based image restoration model
Image Restoration (IR) is a task of reconstructing the latent image from its degraded observations. It has become an important research area in computer vision and image processing, and has wide applications in the imaging industry. Conventional methods apply inverse filtering or optimization-based approaches to restore images corrupted in ideal cases. The limited restoration performance on ill-posed problems and the low-efficient iterative optimization processes prevents such algorithms from being deployed to more complicated industry applications. Recently, the advanced deep Convolutional Neural Networks (CNNs) begin to model the image restoration as learning and inferring the posterior probability in a regression model, and successfully achieved remarkable performance. However, due to the data-driven nature, the models trained with simple synthetic paired data (e.g, bicubic interpolation or Gaussian noises) cannot be well adapted to more complicated inputs from real data domains. Besides, acquiring real paired data for training such models is also very challenging.
In this dissertation, we discuss the data manipulation and model adaptability of the deep learning based image restoration tasks. Specifically, we study improving the model adaptability by understanding the domain difference between its training data and its expected testing data. We argue that the cause of image degradation can be various due to multiple imaging and transmission pipelines. Though complicated to analyze, for some specific imaging problems, we can still improve the performance of deep restoration models on unseen testing data by resolving the data domain differences implied in the image acquisition and formation pipeline. Our analysis focuses on digital image denoising, image restoration from more complicated degradation types beyond denoising and multi-image inpainting. For all tasks, the proposed training or adaptation strategies, based on the physical principle of the degradation formation or based on geometric assumption of the image, achieve a reasonable improvement on the restoration performance.
For image denoising, we discuss the influence of the Bayer pattern of the Camera Filter Array (CFA) and the image demosaicing process on the adaptability of the deep denoising models. Specifically, for the task of denoising RAW sensor observations, we find that unifying and augmenting the data Bayer pattern during training and testing is an efficient strategy to make the well-trained denoising model Bayer-invariant. Additionally, for the RGB image denoising, demosaicing the noisy RAW images with Bayer patterns will result in the spatial-correlation of pixel noises. Therefore, we propose the pixel-shuffle down-sampling approach to break down this spatial correlation, and make the Gaussian-trained denoiser more adaptive to real RGB noisy images.
Beyond denoising, we explain a more complicated degradation process involving diffraction when there are some occlusions on the imaging lens. One example is a novel imaging model called Under-Display Camera (UDC). From the perspective of optical analysis, we study the physics-based imaging processing method by deriving the forward model of the degradation, and synthesize the paired data for both conventional and deep denoising pipeline. Experiments demonstrate the effectiveness of the forward model and the deep restoration model trained with synthetic data achieves visually similar performance to the one trained with real paired images.
Last, we further discuss reference-based image inpainting to restore the missing regions in the target image by reusing contents from the source image. Due to the color and spatial misalignment between the two images, we first initialize the warping by using multi-homography registration, and then propose a content-preserving Color and Spatial Transformer (CST) to refine the misalignment and color difference. We designed the CST to be scale-robust, so it mitigates the warping problems when the model is applied to testing images with different resolution. We synthesize realistic data while training the CST, and it suggests the inpainting pipeline achieves a more robust restoration performance with the proposed CST
A Comparison of Image Denoising Methods
The advancement of imaging devices and countless images generated everyday
pose an increasingly high demand on image denoising, which still remains a
challenging task in terms of both effectiveness and efficiency. To improve
denoising quality, numerous denoising techniques and approaches have been
proposed in the past decades, including different transforms, regularization
terms, algebraic representations and especially advanced deep neural network
(DNN) architectures. Despite their sophistication, many methods may fail to
achieve desirable results for simultaneous noise removal and fine detail
preservation. In this paper, to investigate the applicability of existing
denoising techniques, we compare a variety of denoising methods on both
synthetic and real-world datasets for different applications. We also introduce
a new dataset for benchmarking, and the evaluations are performed from four
different perspectives including quantitative metrics, visual effects, human
ratings and computational cost. Our experiments demonstrate: (i) the
effectiveness and efficiency of representative traditional denoisers for
various denoising tasks, (ii) a simple matrix-based algorithm may be able to
produce similar results compared with its tensor counterparts, and (iii) the
notable achievements of DNN models, which exhibit impressive generalization
ability and show state-of-the-art performance on various datasets. In spite of
the progress in recent years, we discuss shortcomings and possible extensions
of existing techniques. Datasets, code and results are made publicly available
and will be continuously updated at
https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising
methods to investigate their effectiveness, efficiency, applicability and
generalization ability with both synthetic and real-world experiment
Deep Learning Methods for Synthetic Aperture Radar Image Despeckling: An Overview of Trends and Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey
Image denoising is probably the oldest and still one of the most active
research topic in image processing. Many methodological concepts have been
introduced in the past decades and have improved performances significantly in
recent years, especially with the emergence of convolutional neural networks
and supervised deep learning. In this paper, we propose a survey of guided tour
of supervised and unsupervised learning methods for image denoising,
classifying the main principles elaborated during this evolution, with a
particular concern given to recent developments in supervised learning. It is
conceived as a tutorial organizing in a comprehensive framework current
approaches. We give insights on the rationales and limitations of the most
performant methods in the literature, and we highlight the common features
between many of them. Finally, we focus on on the normalization equivariance
properties that is surprisingly not guaranteed with most of supervised methods.
It is of paramount importance that intensity shifting or scaling applied to the
input image results in a corresponding change in the denoiser output
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
Image demosaicking and denoising are the two key fundamental steps in digital
camera pipelines, aiming to reconstruct clean color images from noisy luminance
readings. In this paper, we propose and study Wild-JDD, a novel learning
framework for joint demosaicking and denoising in the wild. In contrast to
previous works which generally assume the ground truth of training data is a
perfect reflection of the reality, we consider here the more common imperfect
case of ground truth uncertainty in the wild. We first illustrate its
manifestation as various kinds of artifacts including zipper effect, color
moire and residual noise. Then we formulate a two-stage data degradation
process to capture such ground truth uncertainty, where a conjugate prior
distribution is imposed upon a base distribution. After that, we derive an
evidence lower bound (ELBO) loss to train a neural network that approximates
the parameters of the conjugate prior distribution conditioned on the degraded
input. Finally, to further enhance the performance for out-of-distribution
input, we design a simple but effective fine-tuning strategy by taking the
input as a weakly informative prior. Taking into account ground truth
uncertainty, Wild-JDD enjoys good interpretability during optimization.
Extensive experiments validate that it outperforms state-of-the-art schemes on
joint demosaicking and denoising tasks on both synthetic and realistic raw
datasets.Comment: Accepted by AAAI202