Video outpainting is a challenging task that generates new video content by extending beyond the boundaries of an original input video, requiring both temporal and spatial consistency. Many existing methods utilize latent diffusion models with U-Net backbones but still struggle to achieve high quality and adaptability in generated content. Diffusion transformers (DiTs) have emerged as a promising alternative because of their superior performance. We introduce OutDreamer, a DiT-based video outpainting framework comprising two main components: a video control branch and a conditional outpainting branch. The video control branch effectively extracts masked video information, while the conditional outpainting branch generates missing content based on these extracted conditions. Additionally, we propose a mask-driven self-attention layer that dynamically integrates the given mask information, further enhancing the model's adaptability to outpainting tasks. Furthermore, we introduce a latent alignment loss to maintain overall consistency both within and between frames. For long video outpainting, we employ a cross-video-clip refiner to iteratively generate missing content, ensuring temporal consistency across video clips. Extensive evaluations demonstrate that our OutDreamer outperforms existing video outpainting methods on widely recognized benchmarks.

Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local light changes. In this paper, we propose DAVIGS, a method that decouples appearance variations in a plug-and-play and efficient manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. Furthermore, our method gathers appearance-related information in 3D space to transform the rendered images, thus building 3D consistency across views implicitly. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds. Additionally, it provides performance improvements for different Gaussian Splatting baselines in a plug-and-play manner.

Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones. While existing methods leveraging large generative models demonstrate impressive results, the high computational cost and latency make them impractical for edge deployment. In this paper, we introduce PocketSR, an ultra-lightweight, single-step model that brings generative modeling capabilities to RealSR while maintaining high fidelity. To achieve this, we design LiteED, a highly efficient alternative to the original computationally intensive VAE in SD, reducing parameters by 97.5\% while preserving high-quality encoding and decoding. Additionally, we propose online annealing pruning for the U-Net, which progressively shifts generative priors from heavy modules to lightweight counterparts, ensuring effective knowledge transfer and further optimizing efficiency. To mitigate the loss of prior knowledge during pruning, we incorporate a multi-layer feature distillation loss. Through an in-depth analysis of each design component, we provide valuable insights for future research. PocketSR, with a model size of 146M parameters, processes 4K images in just 0.8 seconds, achieving a remarkable speedup over previous methods. Notably, it delivers performance on par with state-of-the-art single-step and even multi-step RealSR models, making it a highly practical solution for edge-device applications.

Dual-camera smartphones suffer from geometric and photometric inconsistencies during zoom transitions, primarily due to disparities in intrinsic/extrinsic parameters and divergent image processing pipelines between the two cameras. Existing interpolation methods struggle to effectively address this issue, constrained by the lack of ground-truth datasets and motion ambiguity in dynamic scenarios. To overcome these challenges, we propose OmniZoom, a universal plug-and-play paradigm for cross-device smooth zoom interpolation. Specifically, we present a novel cross-device virtual data generation method utilizing 3D Gaussian Splatting. This method tackles data scarcity by decoupling geometric features via spatial transition modeling and correcting photometric variations with dynamic color adaptation. It is further enhanced by cross-domain consistency learning for device-agnostic semantic alignment. Additionally, we introduce a plug-and-play 3D-TPR (3D Trajectory Progress Ratio Mapping) framework that surmounts 2D spatial limitations. As components of our framework, a texture-focus strategy is introduced for high-frequency detail preservation, incorporating mask penalty constraints to suppress interpolation artifacts. Our pipeline exhibits broad compatibility with diverse interpolation methods and achieves good performance across multiple public benchmarks. Real-world evaluations on various smartphone platforms also reveal significant quality improvements after fine-tuning on our synthetic data, which underscores the robustness and practical effectiveness of our approach for cross-device zoom applications.

From camera to screen, researchers have developed a well-established system for capturing and reproducing the color experience of human eyes. In this study, we aim to upgrade this process by transiting from conventional RGB to multi-spectral image (MSI) color reproduction. While MSI offers evident advantages in color matching, we find out it is not trivial to make good use of more spectral information for color constancy. Therefore, we present a regularized color reproduction system that incorporates a spectral prior-guided optimization strategy to establish a sensor-optimized RGB projection for color matching, along with a learning-based chromatic adaptation model for color constancy. Specifically, we define the RGB projection through an end-to-end optimization under the guidance of sensor spectral sensitivities. Subsequently, we devise a chromatic adaptation neural network that estimates the scene illuminance and an illuminance-adaptive matrix for auto white balancing and dynamic color correction, respectively. Comprehensive experiments show the superiority of our system compared to alternative solutions.

Image resampling is a basic technique that is widely employed in daily applications, such as camera photo editing. Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. Still, these methods are not the perfect substitute for interpolation, due to the drawbacks in efficiency and versatility. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation. Specifically, LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the hyper-parameters that determine the shapes of these resampling functions with a neural network. Based on the formulation of LeRF, we develop a family of models, including both efficiency-orientated and performance-orientated ones. To achieve interpolation-level efficiency, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. On the other hand, to obtain DNN-level performance, we propose an extension of LeRF to enable it in cooperation with pre-trained upsampling models for cascaded resampling. Extensive experiments show that the efficiency-orientated version of LeRF runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3 dB PSNR gain over Bicubic for $\times 2$×2 upsampling on Manga109. Besides, the performance-orientated version of LeRF reaches comparable performance with existing DNNs at much higher efficiency, e.g., less than 25% running time on a desktop GPU.

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts. Furthermore, the system’s modular design facilitates the fast integration of new tasks and models.

Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training resource demands. We present UltraPixel, a novel architecture utilizing cascade diffusion models to generate high-quality images at multiple resolutions (\textit{e.g.}, 1K, 2K, and 4K) within a single model, while maintaining computational efficiency. UltraPixel leverages semantics-rich representations of lower-resolution images in a later denoising stage to guide the whole generation of highly detailed high-resolution images, significantly reducing complexity. Specifically, we introduce implicit neural representations for continuous upsampling and scale-aware normalization layers adaptable to various resolutions. Notably, both low- and high-resolution processes are performed in the most compact space, sharing the majority of parameters with less than 3$\%$ additional parameters for high-resolution outputs, largely enhancing training and inference efficiency. Our model achieves fast training with reduced data requirements, producing photo-realistic high-resolution images and demonstrating state-of-the-art performance in extensive experiments.

Existing semantic segmentation methods improve generalization capability, by regularizing various images to a canonical feature space. While this process contributes to generalization, it weakens the representation inevitably. In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation. Then, the generalization to unseen image styles is achieved by projecting features to this known space. Specifically, we realize the style projection as a weighted combination of stored bases, where the similarity distances are adopted as the weighting factors. Based on the same concept, we extend this process to the decision part of model and promote the generalization of semantic prediction. By measuring the similarity distances to semantic bases (i.e., prototypes), we replace the common deterministic prediction with semantic clustering. Comprehensive experiments demonstrate the advantage of proposed method to the state of the art, up to 3.6% mIoU improvement in average on unseen scenarios. Code and models are available at https://gitee.com/mindspore/models/tree/master/research/cv/SPC-Net.

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x 2 upsampling on Manga109.

In many computer vision applications (e.g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare. In this paper, we aim to achieve object detection on RAW sensor data, which naturally saves the HDR information from image sensors without extra equipment costs. We build a novel RAW sensor dataset, named ROD, for Deep Neural Networks (DNNs)-based object detection algorithms to be applied to HDR data. The ROD dataset contains a large amount of annotated instances of day and night driving scenes in 24-bit dynamic range. Based on the dataset, we first investigate the impact of dynamic range for DNNs-based detectors and demonstrate the importance of dynamic range adjustment for detection on RAW sensor data. Then, we propose a simple and effective adjustment method for object detection on HDR RAW sensor data, which is image adaptive and jointly optimized with the downstream detector in an end-to-end scheme. Extensive experiments demonstrate that the performance of detection on RAW sensor data is significantly superior to standard dynamic range (SDR) data in different situations. Moreover, we analyze the influence of texture information and pixel distribution of input data on the performance of the DNNs-based detector. Code and dataset will be available at https://gitee.com//mindspore/models/tree/master/research/cv/RAOD.

Hyperspectral images consist of multiple spectral channels, and the task of spectral super-resolution is to reconstruct hyperspectral images from 3-channel RGB images, where modeling spectral-wise correlation is of great importance. Based on the analysis of the physical process of this task, we distinguish the spectral-wise correlation into two aspects: similarity and particularity. The Existing Transformer model cannot accurately capture spectral-wise similarity due to the inappropriate spectral-wise fully connected linear mapping acting on input spectral feature maps, which results in spectral feature maps mixing. Moreover, the token normalization operation in the existing Transformer model also results in its inability to capture spectral-wise particularity and thus fails to extract key spectral feature maps. To address these issues, we propose a novel Hybrid Spectral-wise Attention Transformer (HySAT). The key module of HySAT is Plausible Spectral-wise self-Attention (PSA), which can simultaneously model spectral-wise similarity and particularity. Specifically, we propose a Token Independent Mapping (TIM) mechanism to reasonably model spectral-wise similarity, where a linear mapping shared by spectral feature maps is applied on input spectral feature maps. Moreover, we propose a Spectral-wise Re-Calibration (SRC) mechanism to model spectral-wise particularity and effectively capture significant spectral feature maps. Experimental results show that our method achieves state-of-the-art performance in the field of spectral super-resolution with the lowest error and computational costs.

In recent years, denoising methods based on deep learning have achieved unparalleled performance at the cost of large computational complexity. In this work, we propose an Efficient Multi-stage Video Denoising algorithm, called EMVD, to drastically reduce the complexity while maintaining or even improving the performance. First, a fusion stage reduces the noise through a recursive combination of all past frames in the video. Then, a denoising stage removes the noise in the fused frame. Finally, a refinement stage restores the missing high frequency in the denoised frame. All stages operate on a transform-domain representation obtained by learnable and invertible linear operators which simultaneously increase accuracy and decrease complexity of the model. A single loss on the final output is sufficient for successful convergence, hence making EMVD easy to train. Experiments on real raw data demonstrate that EMVD outperforms the state of the art when complexity is constrained, and even remains competitive against methods whose complexities are several orders of magnitude higher. Further, the low complexity and memory requirements of EMVD enable real-time video denoising on commercial SoC in mobile devices.

Convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-definition) or 4K resolution images. To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs. The first CNN extracts haze-relevant features at a reduced resolution of the hazy input and then fits locally-affine models in the bilateral space. Another CNN is used to learn multiple full-resolution guidance maps corresponding to the learned bilateral model. As a result, the feature maps with high-frequency can be reconstructed by multi-guided bilateral upsampling. Finally, the third CNN fuses the high-quality feature maps into a dehazed image. In addition, we create a large-scale 4K image dehazing dataset to support the training and testing of compared models. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing approaches on various benchmarks.

Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e.g., 4K resolution). To address the problem, we propose a novel deep model for fast and accurate UHD Video Deblurring (UHDVD). The proposed UHDVD is achieved by a separable-patch architecture, which collaborates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we design a residual channel-spatial attention (RCSA) module to improve accuracy and reduce the depth of the network appropriately. The proposed UHDVD is the first real-time deblurring model for 4K videos at 35 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Comprehensive experimental results show that our network performs favorably against the state-of-the-art methods on both the 4K dataset and public benchmarks in terms of accuracy, speed, and model size.

Recently, deep learning-based image enhancement algorithms achieved state-of-the-art (SOTA) performance on several publicly available datasets. However, most existing methods fail to meet practical requirements either for visual perception or for computation efficiency, especially for high-resolution images. In this paper, we propose a novel real-time image enhancer via learnable spatial-aware 3dimentional lookup tables(3D LUTs), which well considers global scenario and local spatial information. Specifically, we introduce a light weight two-head weight predictor that has two outputs. One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion. We learn the spatial-aware 3D LUTs and fuse them according to the aforementioned weights in an end-to-end manner. The fused LUT is then used to transform the source image into the target tone in an efficient way. Extensive results show that our model outperforms SOTA image enhancement methods on public datasets both subjectively and objectively, and that our model only takes about 4ms to process a 4K resolution image on one NVIDIA V100 GPU.

Memory requests in many-core systems are interleaved with each other and the locality of many-core systems decreases heavily. Page policies in traditional single core systems are not effective when it comes to many-core systems, because the open-page policy needs much locality of memory requests and the close-page policy takes no advantage of the remaining locality of many-core systems. There are some related memory page management policies, but their high complexity makes them unsuitable to many-core systems. They either need too much modification in operating systems or have large area and power overhead. To overcome these shortcomings of current page policies, in this paper, we propose the row based page policy, that is, RBPP, for the many-core systems, which tracks the row addresses of memory requests to each bank and uses row addresses as the indicator to decide whether or not to close the row buffer when the active memory request finished. We evaluate the proposed RBPP via Gem5 and DRAMSim2, and the results show that row based page policy can decrease the average memory latency by 14.7% and 4.0% over the open-page policy and the close-page policy, respectively. And the area overhead of row based page policy is decreased by 91.4 % and 91.5% over access based page policy and two-level predictor page policy, respectively.

Deterministic replay is a key technique for debugging simultaneous multithreaded programs on multicore processor. With this scheme, software-only implementations generally incur large runtime overhead. Hardware assisted methods can significantly reduce the overhead, but most hardware based recorders are system oriented. They capture all orders happened in monitored application, Operating System, and other applications. This produces inefficiency and inconvenience for application programmers to debug their programs. This paper proposes a hardware assisted recorder (HRUL), which is lightweight and convenient to application programmers. HRUL uses a hybrid hardware-software method to extract dependencies from monitored application in a complex execution environment, and compresses the orders with a combination of online and offline compression algorithm. What' more, It also captures implicit dependencies caused by system call and scheduling in Operating System to make replay faithful. We evaluate the scheme with 16-core runs of PARSEC, our results show that HRUL introduces runtime overhead less than 3% and can reduce log size by 81% (only with online-hardware compression).

GPUs provide powerful computing ability especially for data parallel algorithms. However, the complexity of the GPU system makes the optimization of even a simple algorithm difficult. Different parallel algorithms or optimization methods on a GPU often lead to very different performances. The matrix-vector multiplication routine for general dense matrices (GEMV) is a building block for many scientific and engineering computations. We find that the implementations of GEMV in CUBLAS 4.0 or MAGMA are not efficient, especially for small matrix or fat matrix (a matrix with small number of rows and large number of columns). In this paper, we propose two new algorithms to optimize GEMV on Fermi GPU. Instead of using only one thread, we use a warp to compute an element of vector y. We also propose a novel register blocking method to accelerate GEMV on GPU further. The proposed optimization methods for GEMV are comprehensively evaluated on the matrices with different sizes. Experiment results show that the new methods can achieve over 10x speedup for small square matrices and fat matrices compared to CUBLAS 4.0 or MAGMA, and the new register blocking method can also perform better than CUBLAS 4.0 or MAGMA for large square matrices. We also propose a performance-tuning framework on how to choose an optimal algorithm of GEMV for an arbitrary input matrix on GPU.

It is an important task to tune performance for sparse matrix vector multiplication (SpMV), but it is also a difficult task because of its irregularity. In this paper, we propose a cache blocking method to improve the performance of SpMV on the emerging GPU architecture. The sparse matrix is partitioned into many sub-blocks, which are stored in CSR format. With the blocking method, the corresponding part of vector x can be reused in the GPU cache, so the time spent on accessing the global memory for vector x is reduced heavily. Experimental results on GeForce GTX 480 show that SpMV kernel with the cache blocking method is 5x faster than the unblocked CSR kernel in the best case.

In this paper, we present a Godson-T Verification Engine (GVE) to rapidly prototype and debug our Godson-T many-core processor design. GVE adopts the state-of-the-art hardware platform which contains 6 Xilinx Virtex-5 LX330 FPGAs, thus permitting us to map our many-core processor and peripheral devices into it. Besides the hardware, our toolkit Godson-T Studio provides the compiler, program loader, debugger and monitor to fulfil the purpose of developing, profiling and debugging, while the accuracy loss problem is settled by our novel techniques: Check-point and ILA-Check, presented in this paper. To our experience, GVE greatly reduces the verification cycle due to its high execution speed, for example, it finishes thousands of testcases in an hour, where the software-based approach takes few days to run. And by the help of the checkpoint framework, we can easily locate the faults. Because of these features, GVE makes a great contribution to the 16-tile Godson-T tape-out Project.

The efficient support of cache coherence is extremely important to design and implement many-core processors. In this paper, we propose a synchronization-based coherence (SBC) protocol to efficiently support cache coherence for shared memory many-core architectures. The unique feature of our scheme is that it doesn’t use directory at all. Inspired by scope consistency memory model, our protocol maintains coherence at synchronization point. Within critical section, processor cores record write-sets (which lines have been written in critical section) with bloom-filter function. When the core releases the lock, the write-set is transferred to a synchronization manager. When another core acquires the same lock, it gets the write-set from the synchronization manager and invalidates stale data in its local cache. Experimental results show that the SBC outperforms by averages of 5% in execution time across a suite of scientific applications. At the mean time, the SBC is more cost-effective comparing to directory-based protocol that requires large amount of hardware resource and huge design verification effort.

On-chip many core architecture is an emerging and promising computation platform. High speed on-chip communication and abundant chipped resources are two outstanding advantages of this architecture, which provide an opportunity to implement efficient synchronization scheme. The practical execution efficiency of synchronization scheme is critical to this platform. However, there are few researches on systematic evaluation method of choice synchronization schemes for on-chip many core processors, and effect of dedicated hardware support in this context. So we focus on the evaluation method and criterion of synchronization scheme on the platform. Firstly, we present several criterions proper to on-chip many core architecture, that is, absolute overhead of synchronization operation, the transferring time between different synchronization operations, overhead caused by load imbalance, and the network congestion caused by synchronization operation. Secondly, we illustrate how to design microbenchmarks which one dedicated to evaluate a performance criterion respectively. Finally, we implement these microbenchmarks and synchronization schemes on an on-chip many core processor with shared level-two cache and AMD Opteron commercial chip multi-processor, respectively. And we analyze effect of dedicated hardware support. Results show that the most overhead of synchronization is caused by load imbalance and serialization on synchronization point. It also shows that synchronization scheme supported with dedicated hardware can improve its performance obviously for chipped many-core processor.