Maitreya Suin — accepted AI-conference papers

2026

Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration

Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong, Sina Honari, Amanpreet Walia, Iqbal Mohomed +3

CVPR 2026

Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limi

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2024

CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models

Maitreya Suin, Rama Chellappa

IJCAI 2024poster

Recent generative methods have shown promising blind face restoration performance. They usually project the degraded images to the latent space and then decode high-quality faces either by single-stage latent optimization or directly from the encoding. Generating fine-grained facial details faithful…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)

Snehal Singh Tomar, Maitreya Suin, A. N. Rajagopalan

AAAI 2023technical

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inver…

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2021

Distillation-Guided Image Inpainting

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

ICCV 2021poster

Image inpainting methods have shown significant improvements by using deep neural networks recently. However, many of these techniques often create distorted structures or blurry inconsistent textures. The problem is rooted in the encoder layers' ineffectiveness in building a complete and faithful e…

Cited by 42 PDF ↗Scholar ↗

2021

Gated Spatio-Temporal Attention-Guided Video Deblurring

Maitreya Suin, A. N. Rajagopalan

CVPR 2021poster

Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to m…

Cited by 48 PDF ↗Scholar ↗

2021

Spatially-Adaptive Image Restoration Using Distortion-Guided Networks

Kuldeep Purohit, Maitreya Suin, A. N. Rajagopalan, Vishnu Naresh Boddeti

ICCV 2021poster

We present a general learning-based solution for restoring images suffering from spatially-varying degradations. Prior approaches are typically degradation-specific and employ the same processing across different images and different pixels within. However, we hypothesize that such spatially rigid p…

Cited by 155 PDF ↗code ↗Scholar ↗

2020

Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring

Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan

CVPR 2020poster

This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large recept…

Cited by 345 PDF ↗Scholar ↗