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- Marcelo Bertalmı́oSuggested from co-authorship
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Works55 from public data
- Restormer: Efficient Transformer for High-Resolution Image Restoration4,635
This work proposes an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.
- Transformers in Vision: A Survey3,900
This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline with an introduction to fundamental concepts behind the success of Transformers, i.e., self-attention, large-scale pre-training, and bidirectional feature encoding.
- Multi-Stage Progressive Image Restoration2,345
This paper proposes a novel synergistic design that can optimally balance these competing goals in image restoration tasks, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps.
- Transformers in medical imaging: A survey1,338
This survey surveys the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks and develops taxonomy for each application.
- Learning Enriched Features for Real Image Restoration and Enhancement1,052
This paper presents a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low- resolution representations, named as MIRNet.
- Learning Enriched Features for Fast Image Restoration and Enhancement653
The approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details, and achieves state-of-the-art results for a variety of image processing tasks.
- CycleISP: Real Image Restoration via Improved Data Synthesis460
This paper presents a framework that models camera imaging pipeline in forward and reverse directions that allows any number of realistic image pairs for denoising both in RAW and sRGB spaces and achieves the state-of-the-art performance on real camera benchmark datasets.
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- PromptIR: Prompting for All-in-One Blind Image Restoration212
This work presents a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation, and uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network.
- Striking the Right Balance With Uncertainty199
This paper demonstrates that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples, and presents a novel framework for uncertainty based class imbalance learning that efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers.
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- Burst Image Restoration and Enhancement129
The central idea is to create a set of pseudo-burst features that combine complimentary information from all the input burst frames to seamlessly exchange information, and the approach initially extracts pre-processed features from each burst frame and matches them using an edge-boosting burst alignment module.
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- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
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- Burstormer: Burst Image Restoration and Enhancement Transformer79
This work proposes Burstormer: a novel transformer-based architecture for burst image restoration and enhancement that outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhance-ment.
- Synthesizing the Unseen for Zero-Shot Object Detection75
This work proposes a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them, using a unified model, to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain.
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- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
- Learning digital camera pipeline for extreme low-light imaging42
A new loss function is proposed that exploits the characteristics of both pixel-wise and perceptual metrics, enabling the deep neural network to learn the camera processing pipeline to transform the short-exposure, low-light RAW sensor data to well-exposed sRGB images.
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Publication data from OpenAlex; 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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