Search Results for author: Raimondo Schettini

Found 17 papers, 0 papers with code

Shallow camera pipeline for night photography rendering

no code implementations19 Apr 2022 Simone Zini, Claudio Rota, Marco Buzzelli, Simone Bianco, Raimondo Schettini

We introduce a camera pipeline for rendering visually pleasing photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge.

Image Denoising

Illumination Estimation Challenge: experience of past two years

no code implementations31 Dec 2020 Egor Ershov, Alex Savchik, Ilya Semenkov, Nikola Banić, Karlo Koscević, Marko Subašić, Alexander Belokopytov, Zhihao LI, Arseniy Terekhin, Daria Senshina, Artem Nikonorov, Yanlin Qian, Marco Buzzelli, Riccardo Riva, Simone Bianco, Raimondo Schettini, Sven Lončarić, Dmitry Nikolaev

The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.

Color Constancy

Deep Residual Autoencoder for quality independent JPEG restoration

no code implementations14 Mar 2019 Simone Zini, Simone Bianco, Raimondo Schettini

This results is remarkable since the approaches in the state of the art use a different set of weights for each compression quality, while the proposed model uses the same weights for all of them, making it applicable to images in the wild where the QF used for compression is unkwnown.

SSIM

Multitask Painting Categorization by Deep Multibranch Neural Network

no code implementations19 Dec 2018 Simone Bianco, Davide Mazzini, Paolo Napoletano, Raimondo Schettini

In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation.

Aesthetics Assessment of Images Containing Faces

no code implementations22 May 2018 Simone Bianco, Luigi Celona, Raimondo Schettini

Recent research has widely explored the problem of aesthetics assessment of images with generic content.

Automated Pruning for Deep Neural Network Compression

no code implementations5 Dec 2017 Franco Manessi, Alessandro Rozza, Simone Bianco, Paolo Napoletano, Raimondo Schettini

In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks.

Neural Network Compression Transfer Learning

On the Use of Deep Learning for Blind Image Quality Assessment

no code implementations17 Feb 2016 Simone Bianco, Luigi Celona, Paolo Napoletano, Raimondo Schettini

We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task.

Blind Image Quality Assessment

Single and Multiple Illuminant Estimation Using Convolutional Neural Networks

no code implementations5 Aug 2015 Simone Bianco, Claudio Cusano, Raimondo Schettini

In this paper we present a method for the estimation of the color of the illuminant in RAW images.

How Far Can You Get By Combining Change Detection Algorithms?

no code implementations12 May 2015 Simone Bianco, Gianluigi Ciocca, Raimondo Schettini

Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework.

Change Detection Unity

Color Constancy Using CNNs

no code implementations17 Apr 2015 Simone Bianco, Claudio Cusano, Raimondo Schettini

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination.

Color Constancy

IAT - Image Annotation Tool: Manual

no code implementations18 Feb 2015 Gianluigi Ciocca, Paolo Napoletano, Raimondo Schettini

The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications.

Information Retrieval

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