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Found 27 papers, 21 papers with code
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments.
TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
For channel self-attention, we observe that it may leak the blind-spot information when the channel number is greater than spatial size in the deep layers of multi-scale architectures.
Dual-Camera Smooth Zoom on Mobile Phones
In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview.
Self-Supervised Video Desmoking for Laparoscopic Surgery
On the other hand, in order to enhance the desmoking performance, we further feed the valuable information from PS frame into models, where a masking strategy and a regularization term are presented to avoid trivial solutions.
Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments.
Improving Image Restoration through Removing Degradations in Textual Representations
To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration.
DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
The priors are then regarded as input conditions to maintain reasonable geometries, in which conditional LoRA and weighted score are further proposed to optimize detailed textures.
Learning Real-World Image De-Weathering with Imperfect Supervision
Real-world image de-weathering aims at removing various undesirable weather-related artifacts.
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results.
Self-Supervised Image Restoration with Blurry and Noisy Pairs
By learning in a collaborative manner, the deblurring and denoising tasks in our method can benefit each other.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it.
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks
We show that the proposed subnetwork ensembling method can perform as well as standard deep ensembles in both accuracy and uncertainty estimates, yet with a computational efficiency similar to MC dropout.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks.
Self-Distillation as Instance-Specific Label Smoothing
It has been recently demonstrated that multi-generational self-distillation can improve generalization.
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Deep Adaptive Inference Networks for Single Image Super-Resolution
Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easier regions with less details and the scenarios with restricted efficiency constraints.
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets
Motivated by this, we explore the use of various structured dropout techniques to promote model diversity and improve the quality of probabilistic predictions.
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
Motivated by this, we explore the use of structured dropout to promote model diversity and improve confidence calibration.
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE.
Ranked #19 on
Learning with noisy labels
on CIFAR-10N-Worst
