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- Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction247
A deep Fourier-based Exposure Correction Network (FECNet) consisting of an amplitude subnetwork and a phase sub-network to progressively reconstruct the representation of lightness and structure components is proposed.
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- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Spatial-Frequency Domain Information Integration for Pan-Sharpening158
This paper proposes a Spatial-Frequency Information Integration Network, dubbed as SFIIN, which employs the standard convolution to integrate the local information of two modalities of PAN and MS images in the spatial domain and adopts deep Fourier transformation to achieve the image-wide receptive field for exploring global contextual information.
- FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining155
FreqMamba is proposed, an effective and efficient paradigm that leverages the complementary between Mamba and frequency analysis for image deraining and introduces complementary triple interaction structures including spatial Mamba, frequency-band Mamba, and Fourier global modeling.
- A Real-Time Hand Posture Recognition System Using Deep Neural Networks148
This article proposes a two-stage HPR system for Sign Language Recognition using a Kinect sensor and applies deep neural networks (DNNs) to automatically learn features from hand posture images that are insensitive to movement, scaling, and rotation.
- RETRACTED: Pan-Sharpening with Customized Transformer and Invertible Neural Network145
This is the first attempt to introduce transformer and invertible neural network into pan-sharpening field and outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops.
- A model-based sound localization system and its application to robot navigation132
A mobile robot equipped with a real time sound localization system as well as a sonar system for obstacle detection is described, which is capable of localizing sounding objects in a reverberant environment and approaching the objects without collisions, even when the objects were behind obstacles.
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- Sound localization in reverberant environment based on the model of the precedence effect100
A new algorithm adopted from the echo-avoidance model of the precedence effect was used to detect theecho-free onsets by specifying a generalized pattern of impulse response by integrating the zero-crossing points in different microphones into an azimuth histogram.
- Neural Degradation Representation Learning for All-in-One Image Restoration98
This paper proposes to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations that adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components.
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- Cryptanalysis of a chaotic image encryption scheme based on permutation-diffusion structure57
A novel attack method will be introduced where all the chaotic mappings or parameters which are functionally equivalent to the keys used in the permutation and diffusion stages of the original cryptosystem can fully be revealed and the encrypted images can be completely recovered without knowing the secret keys.
- Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening55
A novel pan-sharpening framework by adaptively learning low-high frequency information integration in the spatial and frequency dual domains by adaptingively learning 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.
- Building ears for robots: Sound localization and separation55
This paper describes the research on bio-mimetic robot audition, which mimicked the cocktail party effect by perceptual grouping of continuous components according to the spatial information obtained by the sound localization method.
- Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices54
This paper presents a novel deep learning based approach—the Range Scaling Global U-Net (RSGUNet)—for perceptual image enhancement on mobile devices that learns a global feature vector as well as a novel range scaling layer that alleviate artifacts in the enhanced images.
- Exposure-Consistency Representation Learning for Exposure Correction53
A proposed ECP module to consistently learn the representation of both underexposure and overexposure in the feature space and an Exposure-Consistency Constraining strategy that augments the various local region exposures and then constrains the feature representation change between the exposure augmented image and the original one are developed.
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- Source-Free Domain Adaptation for Real-World Image Dehazing46
A novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available, and the Domain Representation Normalization (DRN) module to make the representation of real haazi domain features match that of the synthetic domain to bridge the gaps.
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- EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration34
Experimental results demonstrate that the proposed Enhanced Deep Pyramid Network (EDPN) significantly outperforms existing solutions for blurry image super-resolution and blurry image deblocking.
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- Normalization-based Feature Selection and Restitution for Pan-sharpening26
A Normalization-based Feature Selection and Restitution mechanism, which is capable of filtering out the inconsistent features and promoting to learn the consistent ones, thus relieving the over-transferred artifacts in the process of PAN-guided MS super-resolution.
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- AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse17
This work provides a theoretical understanding by analyzing the denoising process through the second-order Adams-Bashforth method, revealing a linear relationship between the outputs of consecutive steps, and proposes a novel caching-based acceleration approach for diffusion models.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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