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- BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation318
A novel framework called BinsFormer, tailored for the classification-regression-based depth estimation, which mainly focuses on two crucial components in the specific task: proper generation of adaptive bins and sufficient interaction between probability distribution and bins predictions.
- When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach248
This paper proposes a convolutional neural network for image denoising which achieves the state-of-the-art performance and proposes a deep neural network solution that cascades two modules for image Denoising and various high-level tasks, respectively, and uses the joint loss for updating only the denoise network via back-propagation.
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- Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon214
A new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting is studied.
- Connecting Image Denoising and High-Level Vision Tasks via Deep Learning204
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.
- Hyperspectral Image Classification in the Presence of Noisy Labels203
Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of the proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive.
- Deep Learning-based Face Super-resolution: A Survey166
This survey presents a comprehensive review of deep learning-based FSR methods in a systematic manner and roughly categorizes existing methods according to the utilization of facial characteristics.
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- A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends107
This survey provides the first in-depth and systematic overview of AiOIR, delivering a structured taxonomy that categorizes existing methods by architectural designs, learning paradigms, and their core innovations, and systematically categorizes current approaches and assess the challenges these models encounter.
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- Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior94
It is observed that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image, and the proposed algorithm is extended to solve more challenging non-uniform blind image deblurring problem.
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- Self-inspired learning for denoising live-cell super-resolution microscopy86
It is shown that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy.
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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