Image Compression Artifacts Research Articles

Spectral properties of bright deposits in permanently shadowed craters on Ceres

Context. Bright deposits in permanently shadowed craters on Ceres are thought to harbor water ice. However, the evidence for water ice presented thus far is indirect. Aims. We aim to directly detect the spectral characteristics of water ice in bright deposits present in permanently shadowed regions (PSRs) in polar craters on Ceres. Methods. We analyzed narrowband images of four of the largest shadowed bright deposits acquired by the Dawn Framing Camera to reconstruct their reflectance spectra, carefully considering issues such as in-field stray light correction and image compression artifacts. Results. The sunlit portion of a polar deposit known to harbor water ice has a negative (blue) spectral slope of −58 ± 12% µm−1 relative to the background in the visible wavelength range. We find that the PSR bright deposits have similarly blue spectral slopes, consistent with a water ice composition. Based on the brightness and spectral properties, we argue that the ice is likely present as particles of high purity. Other components such as phyllosilicates may be mixed in with the ice. Salts are an unlikely brightening agent given their association with cryovolcanic processes, of which we find no trace. Conclusions. Our spectral analysis strengthens the case for the presence of water ice in permanently shadowed craters on Ceres.

Dual-stage feedback network for lightweight color image compression artifact reduction

Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration of the luma channel without considering the chroma components. Besides, most deep convolutional neural networks are hard to deploy in practical applications because of their high model complexity. In this article, we propose a dual-stage feedback network (DSFN) for lightweight color image compression artifact reduction. Specifically, we propose a novel curriculum learning strategy to drive a DSFN to reduce color image compression artifacts in a luma-to-RGB manner. In the first stage, the DSFN is dedicated to reconstructing the luma channel, whose high-level features containing rich structural information are then rerouted to the second stage by a feedback connection to guide the RGB image restoration. Furthermore, we present a novel enhanced feedback block for efficient high-level feature extraction, in which an adaptive iterative self-refinement module is carefully designed to refine the low-level features progressively, and an enhanced separable convolution is advanced to exploit multiscale image information fully. Extensive experiments show the notable advantage of our DSFN over several state-of-the-art methods in both quantitative indices and visual effects with lower model complexity.

Residual Network for Image Compression Artifact Reduction

This paper proposes an image compression algorithm based on Swin Transformer and residual network (STRN), aiming to reduce blurring and distortions in traditionally compressed images. The algorithm utilizes a dual-channel mechanism to remove artifacts from the image, which takes advantage of the complementary features of the transform and residual networks. The Swin Transformer networks address the issue of long-range dependency, leading to an enhanced and improved reconstructed image quality. The residual network is an effective network that mitigates gradient loss and recovers image details during the image compression process. The paper demonstrates that image compression can be achieved by training a convolutional network based on a transformer and residuals network, which significantly reduces artifacts and provides better reconstructed image quality compared to previously used and current mainstream methods based on traditional convolutional neural networks. The proposed approach can remove blocking artifacts by subtracting estimated artifacts from the input image, while still preserving most of the original details. Therefore, our proposed method is highly effective in improving image quality and reducing visual artifacts caused by traditional compression methods. Moreover, this method is useful for enhancing image transmission and storage efficiency in various computer vision systems that employ digital visual codecs.

Sensitivity Decouple Learning for Image Compression Artifacts Reduction.

With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevailing methods only focus on learning a mapping from the compressed image to the original one but ignore the intrinsic attributes of the given compressed images, which greatly harms the performance of downstream parsing tasks. Different from these methods, we propose to decouple the intrinsic attributes into two complementary features for artifacts reduction, i.e., the compression-insensitive features to regularize the high-level semantic representations during training and the compression-sensitive features to be aware of the compression degree. To achieve this, we first employ adversarial training to regularize the compressed and original encoded features for retaining high-level semantics, and we then develop the compression quality-aware feature encoder for compression-sensitive features. Based on these dual complementary features, we propose a Dual Awareness Guidance Network (DAGN) to utilize these awareness features as transformation guidance during the decoding phase. In our proposed DAGN, we develop a cross-feature fusion module to maintain the consistency of compression-insensitive features by fusing compression-insensitive features into the artifacts reduction baseline. Our method achieves an average 2.06 dB PSNR gains on BSD500, outperforming state-of-the-art methods, and only requires 29.7 ms to process one image on BSD500. Besides, the experimental results on LIVE1 and LIU4K also demonstrate the efficiency, effectiveness, and superiority of the proposed method in terms of quantitative metrics, visual quality, and downstream machine vision tasks.

BARRN: A Blind Image Compression Artifact Reduction Network for Industrial IoT Systems

BARRN: A Blind Image Compression Artifact Reduction Network for Industrial IoT Systems

CAIR: Combining integrated attention with iterative optimization learning for sparse-view CT reconstruction

Sparse-view CT is an efficient way for low dose scanning but degrades image quality. Inspired by the successful use of non-local attention in natural image denoising and compression artifact removal, we proposed a network combining integrated attention and iterative optimization learning for sparse-view CT reconstruction (CAIR). Specifically, we first unrolled the proximal gradient descent into a deep network and added an enhanced initializer between the gradient term and the approximation term. It can enhance the information flow between different layers, fully preserve the image details, and improve the network convergence speed. Secondly, the integrated attention module was introduced into the reconstruction process as a regularization term. It adaptively fuses the local and non-local features of the image which are used to reconstruct the complex texture and repetitive details of the image, respectively. Note that we innovatively designed a one-shot iteration strategy to simplify the network structure and reduce the reconstruction time while maintaining image quality. Experiments showed that the proposed method is very robust and outperforms state-of-the-art methods in terms of both quantitative and qualitative, greatly improving the preservation of structures and the removal of artifacts.

Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration.

Wasserstein generative adversarial network (WGAN) has attracted great attention due to its solid mathematical background, i.e., to minimize the Wasserstein distance between the generated distribution and the distribution of interest. In WGAN, the Wasserstein distance is quantitatively evaluated by the discriminator, also known as the critic. The vanilla WGAN trained the critic with the simple Lipschitz condition, which was later shown less effective for modeling complex distributions, like the distribution of natural images. We try to improve the WGAN training by introducing pairwise constraint on the critic, oriented to image restoration tasks. In principle, pairwise constraint is to suggest the critic assign a higher rating to the original (real) image than to the restored (generated) image, as long as such a pair of images are available. We show that such pairwise constraint may be implemented by rectifying the gradients in WGAN training, which leads to the proposed rectified Wasserstein generative adversarial network (ReWaGAN). In addition, we build interesting connections between ReWaGAN and the perception-distortion tradeoff. We verify ReWaGAN on two representative image restoration tasks: single image super-resolution (4× and 8×) and compression artifact reduction, where our ReWaGAN not only beats the vanilla WGAN consistently, but also outperforms the state-of-the-art perceptual quality-oriented methods significantly. Our code and models are publicly available at https://github.com/mahaichuan/ReWaGAN.

Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network (CNN) that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network (CAT-Net) that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.

Robust Image Compression Algorithm using Discrete Fractional Cosine Transform

The discrete fractional Fourier transform become paradigm in signal processing. This transform process the signal in joint time-frequency domain. The attractive and very important feature of DFrCT is an availability of extra degree of one free parameter that is provided by fractional orders and due to which optimization is possible. Less execution time and easy implementation are main advantages of proposed algorithm. The merit of effectiveness of proposed technique over existing technique is superior due to application of discrete fractional cosine transform by which higher compression ratio and PSNR are obtained without any artifacts in compressed images. The novelty of the proposed algorithm is no artifacts in compressed image along with good CR and PSNR. Compression ratio (CR) and peak signal to noise ratio (PSNR) are quality parameters for image compression with optimum fractional order.

COLA-Net: Collaborative Attention Network for Image Restoration

Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available on https://github.com/MC-E/COLA-Net.

Block-Attentive Subpixel Prediction Networks for Computationally Efficient Image Restoration

Image restoration based on the Deep Convolutional Neural Network (CNN) based image restoration has demonstrated promising results in many sub-tasks, such as image super-resolution, compression artifacts removal, image denoising, and image enhancement. Compared to many CNN-based high-level vision tasks that predict sparse probabilities of each class, the CNN for image restoration requires dense pixel-level predictions with precise intensity-level values. Therefore, a minimum number of spatial pooling (or down-sampling) operations is required to maintain the image details. Therefore, designing a fast or lightweight model for image restoration is a difficult problem and is even more critical when the spatial resolution of an image becomes larger. In this paper, we propose a family of networks called Subpixel Prediction Networks (SPNs) that predict reshaped and spatially down-sampled block-wise tensors instead of raw images with full resolution. Under this scheme, spatial downsampling decreases the restoration performance less while making the network faster. We propose a novel Subpixel Block Attention (SBA) module that re-calibrates blockwise features to diminish blockwise discontinuity to increase the performance further. The experimental results reveal that these networks demonstrate good trade-offs between speed (number of computations) and restoration performance in the three image restoration tasks: image compression artifacts removal, color image denoising, and image enhancement.

Deep learning-based compressed image artifacts reduction based on multi-scale image fusion

One of the visually noticeable compression artifacts in block-based image/video compression platforms is called blocking artifact. Several post-processing methods were presented to reduce such kind of artifacts. However, most methods in the literature often induce visibly blurring artifacts. The paper presents a deep network to eliminate image compression artifacts (usually denoted by image deblocking) based on image fusion in multi-scale manner. Recent deep learning-based related methods usually learn deep models using a loss function in per-pixel manner based on explicit image priors in order to directly produce clean images. In place of existing deep learning-guided approaches, the problem is reformulated in this paper to the learning of the residuals (or artifacts) between the received images and their corresponding clean images (ground truths). In the presented deep framework, an input image is first down-sampled while naturally reducing the blocking artifacts. Then, our multi-scale image fusion model is used for fusing the different down-scaled versions (of less artifacts) with the input image (with severer artifacts) to estimate the blocking artifacts. Then, by deducting the estimated artifacts from the input image, the blocking artifacts can be significantly eliminated and most original image details are preserved simultaneously. The presented method is well applicable to any vision-based computer systems with digital visual codec embedded.

Build receptive pyramid for efficient color image compression artifact reduction

A traditional image pyramid is an effective way to extract multiscale features. However, for image restoration, downsampling and upsampling lose details in the original resolution and result in an over smooth result. To overcome this defect, we propose a receptive pyramid (RP) that replaces downsampling by dilated convolution to extract multiscale features on the original resolution and make a full resolution correction. We present an RP-based convolution neural network named receptive pyramid convolutional network (RPCN) for efficient color image compression artifact reduction (CAR). Specifically, we propose residual connected convolution blocks as the baseline of RPCN. RPs work in convolution blocks to extract the hierarchical multiscale features for local feature correction. Moreover, the global feature fusion and vector correction are also introduced to further exploit the hierarchical features from the baseline. Benefiting from the RP, our RPCN achieves state-of-the-art performance with a much smaller model and much faster running speed for the CAR task.

Scale-Wise Convolution for Image Restoration

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g., multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, “scale-wise convolution” is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_sr.

Residual Dense Network for Image Restoration.

Recently, deep convolutional neural network (CNN) has achieved great success for image restoration (IR) and provided hierarchical features at the same time. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images; thereby, resulting in relatively-low performance. In this work, we propose a novel and efficient residual dense network (RDN) to address this problem in IR, by making a better tradeoff between efficiency and effectiveness in exploiting the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via densely connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory mechanism. To adaptively learn more effective features from preceding and current local features and stabilize the training of wider network, we proposed local feature fusion in RDB. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. We demonstrate the effectiveness of RDN with several representative IR applications, single image super-resolution, Gaussian image denoising, image compression artifact reduction, and image deblurring. Experiments on benchmark and real-world datasets show that our RDN achieves favorable performance against state-of-the-art methods for each IR task quantitatively and visually.

MV-GNN: Multi-View Graph Neural Network for Compression Artifacts Reduction

Inevitable compression artifacts in multi-view video (MVV) can clearly degrade the quality of experience in many interaction-oriented 3D visual applications. Under the framework of asymmetric coding, low-quality images can be enhanced with high-quality images from the neighboring viewpoints considering the similarity among different views. However, compression artifacts and warping error cause different cross-view quality gaps for various sequences, and thus the contribution of cross-view priors can hardly be located and considered in previous works. In this paper, we propose a multi-view graph neural network (MV-GNN) to reduce compression artifacts in multi-view compressed images. We dedicate to design a fusion mechanism which can exploit contributions from neighboring viewpoints and meanwhile suppress the misleading information. In our method, a GNN-based fusion mechanism is designed to fuse the cross-view information under the aggregation and update mechanism of GNN. Experiments show that 1.672 dB and 0.0242 average gains on PSNR and SSIM metrics can be obtained, respectively. For the subjective evaluations, blocking effect in the compressed images are clearly suppressed and the damaged object boundary are better recovered. The experimental results demonstrate that our MV-GNN outperforms the state-of-the-art methods.

Manipulation Classification for JPEG Images Using Multi-Domain Features

Image forensics comprises the analyses and classifications of manipulations that have been applied to images. The ability to classify various manipulations that have been employed in the process of forgery is essential. Techniques to identify multiple manipulations applied to uncompressed images have been reported thus far, but the forensic approach for JPEG images compressed with various qualities has not been proposed. In this paper, we propose the manipulation classification network (MCNet) to exploit multi-domain features of the spatial, frequency, and compression domains. The proposed MCNet learns several forensic features for each domain through a multi-stream structure and distinguishes manipulations by comprehensively analyzing the fused features. Our work jointly considers visual artifacts caused by image manipulations and compression artifacts due to JPEG compression; therefore, rich forensic features can be explored and learned in the training phase. To enable forgery analysis in the real-world environment, data were generated based on twenty types of manipulation algorithms and various compression parameters. To demonstrate the effectiveness of the proposed MCNet, extensive experiments were conducted using state-of-the-art baselines. Compared to these baselines, our proposed method outperforms in terms of multi-class manipulation classification. In addition, we experimentally proved that the fine-tuned model based on the multi-class manipulation task was effective for different forensic tasks such as DeepFake detection or integrity authentication of JPEG images.

Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.

CANet: Concatenated Attention Neural Network for Image Restoration

In this article, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this article are as follows: First, we establish a concise network with a recursive topology by abandoning most complex artificially designed topology of the network. Second, we did not use the down-sampling mechanism in our network and a lightweight attention mechanism is adopted for this. Third, we rethink the feature fusion mechanism in the field of image restoration and improve this by applying concise but effective concatenation and feature selection mechanism which promotes further extraction of more cleaner features in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising.

A Comprehensive Benchmark for Single Image Compression Artifact Reduction

We present a comprehensive study and evaluation of existing single image compression artifacts removal algorithms, using a new 4K resolution benchmark including diversified foreground objects and background scenes with rich structures, called Large-scale Ideal Ultra high definition 4K (LIU4K) benchmark. Compression artifacts removal, as a common post-processing technique, aims at alleviating undesirable artifacts such as blockiness, ringing, and banding caused by quantization and approximation in the compression process. In this work, a systematic listing of the reviewed methods is presented based on their basic models (handcrafted models and deep networks). The main contributions and novelties of these methods are highlighted, and the main development directions, including architectures, multi-domain sources, signal structures, and new targeted units, are summarized. Furthermore, based on a unified deep learning configuration (i.e. same training data, loss function, optimization algorithm, etc.), we evaluate recent deep learning-based methods based on diversified evaluation measures. The experimental results show the state-of-the-art performance comparison of existing methods based on both full-reference, non-reference and task-driven metrics. Our survey would give a comprehensive reference source for future research on single image compression artifacts removal and inspire new directions of the related fields.