Generative models trained on extensive high-quality datasets effectively capture the structural and statistical properties of clean images, rendering them powerful priors for transforming degraded features into clean ones in image restoration. VAR, a novel image generative paradigm, surpasses diffusion models in generation quality by applying a next-scale prediction approach. It progressively captures both global structures and fine-grained details through the autoregressive process, consistent with the multi-scale restoration principle widely acknowledged in the restoration community. Furthermore, we observe that during the image reconstruction process utilizing VAR, scale predictions automatically modulate the input, facilitating the alignment of representations at subsequent scales with the distribution of clean images. To harness VAR’s adaptive distribution alignment capability in image restoration tasks, we formulate the multi-scale latent representations within VAR as the restoration prior, thus advancing our delicately designed VarFormer framework. The strategic application of these priors enables our VarFormer to achieve remarkable generalization on unseen tasks while also reducing training computational costs. Extensive experiments underscores that our VarFormer outperforms existing multitask image restoration methods across various restoration tasks. The code is available at https://github.com/siywang541/Varformer.

Multi-modal image fusion aims to generate a fused image by integrating and distinguishing the cross-modality complementary information from multiple source images. While the cross-attention mechanism with global spatial interactions appears promising, it only captures second-order spatial interactions, neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap, we introduce a Synergistic High-order Interaction Paradigm (SHIP), designed to systematically investigate spatial fine-grained and global statistics collaborations between the multi-modal images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication, mathematically equivalent to global interactions, and then foster high-order formats by iteratively aggregating and evolving complementary information, enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean), we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. We further introduce an enhanced version of the SHIP model, called SHIP++ that enhances the cross-modality information interaction representation by the cross-order attention evolving mechanism, cross-order information integration, and residual information memorizing mechanism. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies, leading in superior performance over state-of-the-art alternatives, as shown through comprehensive experiments across various benchmarks in two significant multi-modal image fusion tasks: pan-sharpening, and infrared and visible image fusion.

Pan-sharpening is a super-resolution problem that essentially relies on spectra fusion of panchromatic (PAN) images and low-resolution multi-spectral (LRMS) images. The previous methods have validated the effectiveness of information fusion in the Fourier space of the whole image. However, they haven't fully explored the Fourier relationships at different hierarchies between PAN and LRMS images. To this end, we propose a Hierarchical Frequency Integration Network (HFIN) to facilitate hierarchical Fourier information integration for pan-sharpening. Specifically, our network consists of two designs: information stratification and information integration. For information stratification, we hierarchically decompose PAN and LRMS information into spatial, global Fourier and local Fourier information, and fuse them independently. For information integration, the above hierarchical fused information is processed to further enhance their relationships and undergo comprehensive integration. Our method extend a new space for exploring the relationships of PAN and LRMS images, enhancing the integration of spatial-frequency information. Extensive experiments robustly validate the effectiveness of the proposed network, showcasing its superior performance compared to other state-of-the-art methods and generalization in real-world scenes and other fusion tasks as a general image fusion framework. Code is available at https://github.com/JosephTiTan/HFIN.

Infrared and visible image fusion aims to generate a fused image by integrating and distinguishing complementary information from multiple sources. While the cross-attention mechanism with global spatial interactions appears promising, it only capture second-order spatial inter-actions, neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the ex-ploitation of synergies between multi-modalities. To bridge this gap, we introduce a Synergistic High-order Interaction Paradigm (SHIP), designed to systematically investigate the spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication, mathematically equivalent to global interactions, and then foster high-order formats by iteratively aggregating and evolving complementary information, enhancing both efficiency andflexibility; 2) Channel dimension: expanding on channel interactions with first-order statistics (mean), we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies, leading to superior performance over state-of-the-art alternatives, as shown through comprehensive experiments across various benchmarks. Code is available at https://github.com/zheng980629/SHIP.

Video deblurring is a challenging task because only input blurry sequences are available. To further constrain the optimization process, existing methods explore various additional information,e.g., events, depth and sharpness prior. However, they consume large computing costs or generate unpleasant visual results due to the insufficient exploitation of spatio-temporal information. In this work, we develop a novel spatio-temporal sharpness map learned by a prior-based generation network implicitly. The proposed generation network blends both spatial and temporal sharpness priors in a blurry sequence, while few extra parameters are added. We show that the proposed map has better spatial continuity and guidance for video deblurring than the previous method. Furthermore, different from the simply concatenation in the previous work, we allow the sharpness map to accommodate to more effective video deblurring via a dual-stream network. Specifically, the network is decomposed by two branches, namely inter-frame and intra-frame reconstructions. The inter-frame reconstruction obtains the sharp patches of cecutive frames from the sharpness map to restore textures well. Meanwhile, the other intra-frame branch is responsible for recovering structures of the latent frame, where a novel histogram statistical method is developed to quantify and count textures in the feature under the modulation of the sharpness map. Quantitative and qualitative experiments successfully validate the effectiveness of our proposed method.

Images corrupted with degradations often result in a performance drop in downstream image recognition models trained on clean images. Previous image restoration (IR) methods either restore the images without delicately considering the semantic recovery, or the training objectives cannot meet unseen recognition models, leading to poor and non-generalizable performance for various downstream recognition tasks. In this paper, we propose a general image restoration framework for visual recognition, IRVR, which is addressed for generalized and effective semantic recovery in image restoration for a range of high-level tasks. Concretely, for better generalization, we train the IR models with semantic recovery as the primary objective, and image regression as a regularization term, respectively, where the primary objective gradient is calibrated with the regularization gradient to ensure the generalization of IR to unseen recognition models. For effectiveness, we introduce an intrinsic semantic consistency constraint to match the semantic statistical distribution between restored and clean image pairs. Our IRVR is recognition-agnostic and orthogonal to IR, making it a plug-and-play component that can be incorporated into existing IR methods without adding any computation cost during inference. Extensive experiments demonstrate the effectiveness and generalization of our IRVR for improving the performance of IR in diverse downstream high-level tasks. The IRVR's ability to accurately recover intrinsic semantics in images is instrumental in high-level machine analysis, which ensures the integrity and authenticity of multimedia content.

Low-light images suffer severe degradation of low lightness and noise corruption, causing unsatisfactory visual quality and visual recognition performance. To solve this problem while meeting the unavailability of paired datasets in wide-range scenarios, unsupervised low-light image enhancement (ULLIE) techniques have been developed. However, these methods are primarily guided to alleviate the degradation effect on visual quality rather than semantic levels, hence limiting their performance in visual recognition tasks. To this end, we propose to learn a Semantic Degradation-Aware Guidance (SDAG) that perceives the low-light degradation effect on semantic levels in a self-supervised manner, which is further utilized to guide the ULLIE methods. The proposed SDAG utilizes the low-light degradation factors as augmented signals to degrade the low-light images, and then capture their degradation effect on semantic levels. Specifically, our SDAG employs the subsequent pre-trained recognition model extractor to extract semantic representations, and then learns to self-reconstruct the enhanced low-light image and its augmented degraded images. By constraining the relative reconstruction effect between the original enhanced image and the augmented formats, our SDAG learns to be aware of the degradation effect on semantic levels in a relative comparison manner. Moreover, our SDAG is general and can be plugged into the training paradigm of the existing ULLIE methods. Extensive experiments demonstrate its effectiveness for improving the ULLIE approaches on the downstream recognition tasks while maintaining a competitive visual quality. Code will be available at https://github.com/zheng980629/SDAG.

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a mini-batch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.

Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct end-to-end mapping networks heuristically, neglecting the intrinsic prior of image enhancement task and lacking transparency and interpretability. Although some unfolding solutions have been proposed to relieve these issues, they rely on proximal operator networks that deliver ambiguous and implicit priors. In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm. Motivated by the powerful feature representation capability of Masked Autoencoder (MAE), we customize MAE-based illumination and noise priors and redevelop them from two perspectives: 1) structure flow: we train the MAE from a normal-light image to its illumination properties and then embed it into the proximal operator design of the unfolding architecture; and 2) optimization flow: we train MAE from a normal-light image to its gradient representation and then employ it as a regularization term to constrain noise in the model output. These designs improve the interpretability and representation capability of the model. Extensive experiments on multiple low-light image enhancement datasets demonstrate the superiority of our proposed paradigm over state-of-the-art methods. Code is available at https://github.com/zheng980629/CUE.

The success of deep neural networks for pan-sharpening is commonly in a form of black box, lacking transparency and interpretability. To alleviate this issue, we propose a novel model-driven deep unfolding framework with image reasoning prior tailored for the pan-sharpening task. Different from existing unfolding solutions that deliver the proximal operator networks as the uncertain and vague priors, our framework is motivated by the content reasoning ability of masked autoencoders (MAE) with insightful designs. Specifically, the pre-trained MAE with spatial masking strategy, acting as intrinsic reasoning prior, is embedded into unfolding architecture. Meanwhile, the pre-trained MAE with spatial-spectral masking strategy is treated as the regularization term within loss function to constrain the spatial-spectral consistency. Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability. The uniqueness of our framework is that the holistic learning process is explicitly integrated with the inherent physical mechanism underlying the pan-sharpening task. Extensive experiments on multiple satellite datasets demonstrate the superiority of our method over the existing state-of-the-art approaches. Code will be released at https://manman1995.github.io/.

Video deblurring aims to restore the latent video frames from their blurred counterparts. Despite the remarkable progress, most promising video deblurring methods only investigate the temporal priors in the spatial domain and rarely explore their its potential in the frequency domain. In this paper, we revisit the blurred sequence in the Fourier space and figure out some intrinsic frequency-temporal priors that imply the temporal blur degradation can be accessibly decoupled in the potential frequency domain. Based on these priors, we propose a novel Fourier-based frequency-temporal video deblurring solution, where the core design accommodates the temporal spectrum to a popular video deblurring pipeline of feature extraction, alignment, aggregation, and optimization. Specifically, we design a Spectrum Prior-guided Alignment module by leveraging enlarged blur information in the potential spectrum to mitigate the blur effects on the alignment. Then, Temporal Energy prior-driven Aggregation is implemented to replenish the original local features by estimating the temporal spectrum energy as the global sharpness guidance. In addition, the customized frequency loss is devised to optimize the proposed method for decent spectral distribution. Extensive experiments demonstrate that our model performs favorably against other state-of-the-art methods, thus confirming the effectiveness of frequency-temporal prior modeling.

Ultra-high-definition (UHD) image enhancement is a challenging problem that aims to effectively and efficiently recover clean UHD images. To maintain efficiency, the straightforward approach is to downsample and perform most computations on low-resolution images. However, previous studies typically rely on the uniform and content-agnostic downsampling method that equally treats various regions regardless of their complexities, thus limiting the detail reconstruction in UHD image enhancement. To alleviate this issue, we propose a novel spatial-variant and invertible non-uniform downsampler that adaptively adjusts the sampling rate according to the richness of details. It magnifies important regions to preserve more information (e.g., sparse sampling points for sky, dense sampling points for buildings). Therefore, we propose a novel Non-uniform-Sampling Enhancement Network (NSEN) consisting of two core designs: 1) content-guided downsampling that extracts texture representation to guide the sampler to perform content-aware downsampling for producing detail-preserved low-resolution images; 2) invertible pixel-alignment which remaps the forward sampling process in an iterative manner to eliminate the deformations caused by the non-uniform downsampling, thus producing detail-rich clean UHD images. To demonstrate the superiority of our proposed model, we conduct extensive experiments on various UHD enhancement tasks. The results show that the proposed NSEN yields better performance against other state-of-the-art methods both visually and quantitatively.

Image restoration techniques, spanning from the convolution to the transformer paradigm, have demonstrated robust spatial representation capabilities to deliver high-quality performance.Yet, many of these methods, such as convolution and the Feed Forward Network (FFN) structure of transformers, primarily leverage the basic first-order channel interactions and have not maximized the potential benefits of higher-order modeling. To address this limitation, our research dives into understanding relationships within the channel dimension and introduces a simple yet efficient, high-order channel-wise operator tailored for image restoration. Instead of merely mimicking high-order spatial interaction, our approach offers several added benefits: Efficiency: It adheres to the zero-FLOP and zero-parameter principle, using a spatial-shifting mechanism across channel-wise groups. Simplicity: It turns the favorable channel interaction and aggregation capabilities into element-wise multiplications and convolution units with $1 \times 1$ kernel. Our new formulation expands the first-order channel-wise interactions seen in previous works to arbitrary high orders, generating a hierarchical receptive field akin to a Rubik's cube through the combined action of shifting and interactions. Furthermore, our proposed Rubik's cube convolution is a flexible operator that can be incorporated into existing image restoration networks, serving as a drop-in replacement for the standard convolution unit with fewer parameters overhead. We conducted experiments across various low-level vision tasks, including image denoising, low-light image enhancement, guided image super-resolution, and image de-blurring. The results consistently demonstrate that our Rubik's cube operator enhances performance across all tasks. Code is publicly available at https://github.com/zheng980629/RubikCube.

The blooming progress made in deep learning-based image restoration has been largely attributed to the availability of high-quality, large-scale datasets and advanced network structures. However, optimization functions such as L_1 and L_2 are still de facto. In this study, we propose to investigate new optimization functions to improve image restoration performance. Our key insight is that ``random weight network can be acted as a constraint for training better image restoration networks''. However, not all random weight networks are suitable as constraints. We draw inspiration from Functional theory and show that alternative random weight networks should be represented in the form of a strict mathematical manifold. We explore the potential of our random weight network prototypes that satisfy this requirement: Taylor's unfolding network, invertible neural network, central difference convolution, and zero-order filtering. We investigate these prototypes from four aspects: 1) random weight strategies, 2) network architectures, 3) network depths, and 4) combinations of random weight networks. Furthermore, we devise the random weight in two variants: the weights are randomly initialized only once during the entire training procedure, and the weights are randomly initialized in each training epoch. Our approach can be directly integrated into existing networks without incurring additional training and testing computational costs. We perform extensive experiments across multiple image restoration tasks, including image denoising, low-light image enhancement, and guided image super-resolution to demonstrate the consistent performance gains achieved by our method. Upon acceptance of this paper, we will release the code.

Spatial down-sampling techniques, such as strided convolution, Gaussian, and Nearest down-sampling, are essential in deep neural networks. In this study, we revisit the working mechanism of the spatial down-sampling family and analyze the biased effects caused by the static weighting strategy employed in previous approaches. To overcome this limitation, we propose a novel down-sampling paradigm in the Fourier domain, abbreviated as FouriDown, which unifies existing down-sampling techniques. Drawing inspiration from the signal sampling theorem, we parameterize the non-parameter static weighting down-sampling operator as a learnable and context-adaptive operator within a unified Fourier function. Specifically, we organize the corresponding frequency positions of the 2D plane in a physically-closed manner within a single channel dimension. We then perform point-wise channel shuffling based on an indicator that determines whether a channel's signal frequency bin is susceptible to aliasing, ensuring the consistency of the weighting parameter learning. FouriDown, as a generic operator, comprises four key components: 2D discrete Fourier transform, context shuffling rules, Fourier weighting-adaptively superposing rules, and 2D inverse Fourier transform. These components can be easily integrated into existing image restoration networks. To demonstrate the efficacy of FouriDown, we conduct extensive experiments on image de-blurring and low-light image enhancement. The results consistently show that FouriDown can provide significant performance improvements. We will make the code publicly available to facilitate further exploration and application of FouriDown.

Underexposed images inevitably suffer severe degradation due to light distortion and noise corruption. Motivated by the limited samples of paired datasets, several unsupervised enhancement methods have been developed. However, these techniques heavily rely on pre-defined fixed lightness and noise removal constraints. Correspondingly, they cannot match the image-specific lightness when performing enhancement and can only refine details in a non-perceptual way. In this paper, we propose an Unsupervised Underexposed Image Enhancement Network (U2IENet) with self-illuminated and perceptual guidance. Specifically, to adjust the illumination for matching the image-specific lightness adaptively, we utilize the bright area of the underexposed image as the self-illuminated guidance to constrain the training process and modulate the features. Meanwhile, we introduce the perceptual guidance as a constraint to remove the noise based on illumination distribution, thus refining the details perceptually. Experiments on both underexposed datasets and public low-light datasets demonstrate the superiority of the proposed approach with higher flexibility over state-of- the-art solutions. In addition, our U2IENet also provides a side function that enables users to adjust the lightness via interactive tuning of a single parameter.

Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic. Motivated by this, several personalized enhancement methods have been investigated. However, the enhancement process based on user preferences in these techniques is invisible, i.e., a "black box". In this work, we propose an intelligible unsupervised personalized enhancer (iUP-Enhancer) for low-light images, which establishes the correlations between the low-light and the unpaired reference images with regard to three user-friendly attributions (brightness, chromaticity, and noise). The proposed iUP-Enhancer is trained with the guidance of these correlations and the corresponding unsupervised loss functions. Rather than a "black box" process, our iUP-Enhancer presents an intelligible enhancement process with the above attributions. Extensive experiments demonstrate that the proposed algorithm produces competitive qualitative and quantitative results while maintaining excellent flexibility and scalability. This can be validated by personalization with single/multiple references, cross-attribution references, or merely adjusting parameters.

Pan-sharpening aims to generate high-spatial resolution multi-spectral (MS) image by fusing high-spatial resolution panchromatic (PAN) image and its corresponding low-spatial resolution MS image. Despite the remarkable progress, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solutions in the frequency domain. In this paper, we propose a novel pan-sharpening framework by adaptively learning low-high frequency information integration in the spatial and frequency dual domains. It consists of three key designs: mask prediction sub-network, low-frequency learning sub-network and high-frequency learning sub-network. Specifically, the first is responsible for measuring the modality-aware frequency information difference of PAN and MS images and further predicting the low-high frequency boundary in the form of a two-dimensional mask. In view of the mask, the second adaptively picks out the corresponding low-frequency components of different modalities and then restores the expected low-frequency one by spatial and frequency dual domains information integration while the third combines the above refined low-frequency and the original high-frequency for the latent high-frequency reconstruction. In this way, the low-high frequency information is adaptively learned, thus leading to the pleasing results. Extensive experiments validate the effectiveness of the proposed network and demonstrate the favorable performance against other state-of-the-art methods. The source code will be released at https://github.com/manman1995/pansharpening.