TL;DR
The confidence-guided hybrid network (CGHNet) is proposed, a parallel three-branch framework that jointly performs frequency-decoupled local restoration, global context modeling, and pixel-wise degradation prior estimation and its key component is a confidence-guided feature purification mechanism.
An efficient aligned kernel network (AKNet) is proposed, which innovatively employs super-large convolution kernels to capture global receptive fields with minimal computational overhead, effectively mimicking the long-range dependency modeling of transformers.
In optical measurement environments for weapons system test and evaluation, imagery is frequently degraded by haze and smoke, impairing downstream analysis. Existing methods rely on physics-based models or single-image deep learning, both struggling under non-uniform haze or recovering occluded structures. This paper proposes a residual-based restoration network for fixed-camera settings where a clean reference image exists before degradation. Restoration is redefined as estimating the change relative to the reference rather than reconstructing all pixels. Three key designs are introduced: a shared-encoder dual-input structure with absolute-difference skip connections focusing on changed regions; a dual global context fusion module injecting a FiLM-conditioned global vector and spatial change map into the bottleneck and final decoder; and a multi-objective loss combining Charbonnier-SSIM, change-weighted reconstruction, and mask-based residual alignment. Experiments in the target fixed-camera reference-guided setting demonstrate substantial gains over single-image baselines.
Joint demosaicing and denoising is a fundamental step in camera image signal processing, yet remains challenging because different Bayer-like color filter arrays (CFAs) and sensor noise jointly corrupt both color sampling and image content. Existing unified restoration networks explicitly model CFA geometry but are still driven primarily by pixel-level supervision, making them prone to structural degradation around edges, repetitive textures, and moir\'e patterns where local evidence is unreliable. We attribute this limitation partly to the absence of explicit structural guidance beyond pixel-level reconstruction supervision. Motivated by this observation, we propose a structural-guided unified restoration framework that injects pretrained structural knowledge into CFA-aware image restoration. Our model receives a unified five-channel observation consisting of the raw mosaic, CFA masks, and a noise-level map. A SwinIR restoration branch reconstructs pixel details under CFA-conditioned modulation, while a parallel structural reasoning branch extracts complementary structural cues from a sparse pseudo-RGB observation. To bridge the substantial domain gap between sparse noisy sensor data and the natural-image pretraining domain of the structural encoder, we introduce a lightweight trainable adapter before residually fusing structural and restoration features. A shared decoder jointly predicts the restored RGB image and an auxiliary clean mosaic, providing supervision in both image and sensor domains. Extensive experiments across multiple CFA patterns and noise levels demonstrate consistent improvements over state-of-the-art unified and CFA-specific methods, indicating that adapted structural priors can enhance robust camera image restoration. The source codes and dataset are provided in the supplementary material.
High-Compression videos suffer from severe distortions, among which degradation in person regions has the greatest impact on viewers’ immersive experience. Existing quality enhancement techniques usually focus on overall image denoising or super-resolution, often overlooking the crucial recovery of fine structures in these essential person regions. To address these challenges, the research introduces a novel framework titled Person Region Restoration Driven by Perceptual Fidelity (PRRDPF), which combines long-range dependency features with perceptual structure loss for enhanced generative restoration. Specifically, first, the research constructs a high-fidelity distorted person-region dataset via a closed-loop degradation pipeline, addressing the lack of paired datasets. Secondly, a Temporal Gated Fusion (TGF) block is designed to use gated convolutions for selectively recovering high-frequency features while capturing local and global dependencies. Finally, a Structural Similarity Index Measure (SSIM)-based dynamic weighted adversarial loss is proposed to prioritize the restoration of visual texture details. Experimental results validate that PRRDPF significantly outperforms the best models in Peak Signal-to-Noise Ratio (PSNR), SSIM, and Learned Perceptual Image Patch Similarity (LPIPS), effectively mitigating artifacts and enhancing clarity in person visuals. This framework presents a promising approach for intelligent video coding integrated with generative artificial intelligence and holds significant potential for practical applications.
FIT employs a lightweight Degradation Encoder to predict a global degradation vector and a spatial degradation map from local degradation severity, which jointly condition the patch embedding and unembedding through adaptive deformation, and introduces a task-token dropout strategy that regularizes task conditioning during training.
MGN-AIR is presented, a novel pixel-level restoration framework for all-in-one image restoration that leverages both textual and visual prompts to provide global and local degradation cues, guiding the model on where to look and how to restore at each pixel.
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